Release 2023-2

Library Background

Release Notes

Release 2023-2

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Added support for interactive nonstandard protein residue mutation via the Workspace context menu [2023-2]
  • Save up to 5K resolution GIFs (Workspace -> Animate) [2023-2]
  • Reduce unexpected visual clutter with new preference to limit the number of atom labels [2023-2]
  • Maestro to PyMOL connection [2023-2]
    • Create a simple PyMOL movie from Maestro scenes
  • Maestro to LiveDesign connection [2023-2]
    • Connect to LiveDesign servers with new access point and connection status indicator
    • Retain and reuse single sign on (SSO) tokens to streamline the login process
    • Benefit from a streamlined process of pushing Maestro data into LiveDesign via the Export panel
  • Export Structures – New option to export MD-ready structure / trajectory to CMS file [2023-2]
  • New “Show Atom Properties (beta)” panel [2023-2]
    • List multiple atom properties for selected atoms
    • Display SMARTS/SMILES patterns for contiguous selected atoms, including the SMARTS index of the hovered-over atom

Workflows & Pipelining [KNIME Extensions]

  • Includes the latest version of KNIME [2023-2]
  • Improved usability of the Extract Properties node configuration panel [2023-2]
  • Updated LiveDesign nodes and protocols: [2023-2]
    • Adaptable input column checking of the LiveDesign input node
    • KNIME protocol section to install extra KNIME extensions is more robust

Target Validation & Structure Enablement

Protein Preparation

  • Bond orders and charges can now either be re-assigned or only added to ligands and residues with missing bond orders [2023-2]

Protein X-Ray Refinement

  • Improved robustness when running Phenix/OPLS [2023-2]
  • New plots to inspect the best refinement statistics from Phenix/OPLS weight scans [2023-2]
  • Phenix/OPLS weight scan automatically choses a near-optimal combination of refinement parameters [2023-2]

AlphaFold Download / Process

  • Downloaded AlphaFold structures are returned with an automatically created heatmap of the PAE matrix [2023-2]
  • Processed AlphaFold structures can now be optionally capped and the pLDDT threshold changed [2023-2]

Multiple Sequence Viewer/Editor

  • New ability for the detection and annotation of Vernier zone residues in antibodies [2023-2]

IFD-MD

  • Added visual indicator when the target ligand is missing torsional parameters [2023-2]

Desmond Molecular Dynamics

Improved plotting for Trajectory Plots [2023-2]

Hit Identification & Virtual Screening

Ligand Preparation and Conformation Generation

  • Full support for Epik 7 pKa predictions within the LigPrep interface and command line invocation [2023-2]

Empirical and QM-based pKa Prediction

  • Conjugate acid/base labels are now included in the Epik 7 log file [2023-2]

Hit Analysis

  • Release of the new Hit Analysis interface to streamline interactive analysis and selection of hits from virtual screening campaigns based on molecular properties, ligand feature locations, and shape alignments [2023-2]

Active Learning Applications

  • Added ability to train on preexisting FEP data for relative and absolute AL-FEP [2023-2]
  • Added histogram of compounds prioritized by machine learning for relative and absolute AL-FEP [2023-2]
  • Modified default machine learning settings to improve out-of-the-box performance of relative and absolute AL-FEP [2023-2]

ABFEP

  • New capability for fast filtering of inactive ligands to dramatically improve ABFEP throughput [2023-2]

Lead Optimization

Macrocycles

  • Improved atom mapping in FEP+ maps for macrocycles [2023-2]

FEP+

  • New functionallity to auto-populate state populations in the FEP+ interface [2023-2]
  • Improved reporting of results in a single column (val ± err) in the overview tab of the FEP+ interface [2023-2]
  • Option to automatically merge force field parameters generated during an FEP+ job with those defined in your Maestro preferences [2023-2]
  • Option to select alternative water models for Relative Binding FEP+ via Advanced settings [2023-2]
  • Fast filtering of inactive ligands to dramatically improve ABFEP throughput [2023-2]
  • Improved atom mapping in FEP+ maps for macrocycles [2023-2]

Protein FEP

  • Ability to simultaneously predict protein thermostability with every protein/ligand selectivity simulation [2023-2]
  • Option to interactively edit perturbation topologies for protein residue mutations in FEP+ interface [2023-2]

Constant pH Simulations (Beta)

  • Greatly improved accuracy in protein pKa predictions through improved sampling of the physical end states of titratable residue side chains in FEP+ [2023-2]

Quantum Mechanics

  • New ability to plot excited state energies in rigid and relaxed coordinate scans [2023-2]
  • Support for alignment based on uniform scaling in the VCD/IR spectrum_align utility [2023-2]

Semi-Empirical Quantum Mechanics

  • Capability to use GFN2-xTB from within Jaguar and in Jaguar-based workflows [2023-2]

Biologics Drug Discovery

  • Improved protein descriptor calculation throughput with ability to run in parallel over multiple CPUs [2023-2]
  • Up to 5x speedup in protein surface calculations [2023-2]
  • Modeling of single-chain Fvs is now incorporated into the antibody structure-prediction interface [2023-2]
  • Modeling of F(ab)2 formats is now integrated into the antibody structure-prediction interface [2023-2]
  • MSV is now accessible directly from the protein-protein docking interface [2023-2]
  • Selected entries in antibody database management interface can now be exported to MSV and Maestro [2023-2]

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Quantum ESPRESSO: Reduced disk usage with hybrid functionals [2023-2]
  • Quantum ESPRESSO: Option to import only the final structure in QE import GUI [2023-2]
  • Quantum ESPRESSO: Automatic q-point mesh setup with hybrid functional [2023-2]

Transport Calculations via MD simulations

Product: MS Transport

  • Diffusion Coefficient Viewer: Visualization of atoms selected for diffusion tracing [2023-2]
  • Viscosity: Expanded range of shear stress available for analysis [2023-2]

Materials Informatics

Product: MS Informatics

  • Machine Learning Property: Report of entries with failed predictions if any [2023-2]
  • Machine Learning Property: Density prediction for molecular liquids [2023-2]
  • Machine Learning Property: Models to measure uncertainties from the predicted properties [2023-2]

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • CG FF Builder: Better defaults for convergence [2023-2]
  • CG FF Builder: Option to set initial values [2023-2]
  • CG FF Builder: Option to save the force field file in viewer [2023-2]

Dielectric Properties

Product: MS Dielectric

  • Complex Permittivity: Option to adjust the length of dipole moment sample extracted from the source trajectory (command line) [2023-2]

MS Maestro Builders and Tools

  • Crystal: Edit option for lattice parameters when importing PDB without them [2023-2]
  • Manipulate Cell: Option to take multiple entries as input for selected operations [2023-2]
  • Meta Workflows: Support for molecular QM simulation stages [2023-2]
  • Meta Workflows: Option to compute ESP charges for subsequent stages [2023-2]
  • Semicrystalline Polymer: Improved robustness and speed for building semicrystal interface [2023-2]

Classical Mechanics

  • Crystal Morphology: Improved pop-up guideline for setting a proper input cell size [2023-2]
  • Improved UI to select atoms for substrate restraints in MD-based workflow panels [2023-2]
  • Droplet: Option to take existing MD simulation trajectory as input [2023-2]
  • Droplet: Support for built-in and custom solvents for contact angle prediction [2023-2]
  • Electroporation: Workflow module to simulate and assess membrane electroporation [2023-2]
  • Evaporation: Redesigned UI for the workflow setup panel [2023-2]
  • Evaporation: Support for evaporating multiple solvents [2023-2]
  • Evaporation: Added flexibility to evaporation zone definition [2023-2]
  • Evaporation Results: Load structures from one or more iterations into the Project Table [2023-2]
  • MD Multistage: Support for negative external electric field [2023-2]
  • MD Multistage: Option to remove center of mass velocity [2023-2]
  • Stress Strain: Support for sinusoidal loading (command line) [2023-2]

Quantum Mechanics

  • Adsorption Enumeration: Option to set bridging and hollow sites for adsorption [2023-2]
  • Complex Enumeration: Report of the ligand exchange stability in Project Table [2023-2]
  • Prediction of singlet excitation energy transfer rates (SEET) (command line) [2023-2]

Education Content

  • New Tutorial: Calculating Voltage Curves on Spinel Intercalation Compounds [2023-2]
  • New Tutorial: Machine Learning for Ionic Conductivity [2023-2]
  • New Tutorial: Electroporation [2023-2]
  • Update: Evaporation [2023-2]
  • Update: Droplet Contact Analysis [2023-2]
  • Update: Viscosity [2023-2]
  • Update: Machine Learning Property Prediction [2023-2]

LiveDesign

What’s New in 2023-2

Kubernetes versions of LiveDesign include:

  • Composite Rows
    • Create relationships between entities to better view the composition of complex mixtures, linking them as subcomponents and showing them as indented rows.
    • Get a better understanding of drug formulations by viewing the components that make up the mixture
    • Better define the composition of stereoisomeric mixtures
    • Analyze the differences between different battery electrolyte formulations

All versions of LiveDesign include:

  • Forms
    • Show data in a custom, dense arrangement with the Matrix widget
    • Search for compound IDs directly in the Compound Image widget
    • Set up forms more quickly, and identify columns to add to widgets more quickly, with an updated column shuttle
    • Collapse or expand all swimlanes in Kanban widgets using a menu option
    • Form widget titles automatically expand to show longer widget titles when a single widget is within a window
  • View any custom Experimental Metadata in the assay tooltips
    • Any metadata can be added to LiveDesign from corporate assay capture systems through the Data Integrator
  • Generic Entity – store, model, and analyze any kind of modality in LiveDesign
    • Purge and Overwrite experimental data
    • Append new data to a Lot
  • 3D Visualizer
    • View halogen bond and salt bridge interactions
    • Show Chain ID in the residue label
  • Significant improvements to streamlining integration of Maestro sessions and LiveDesign servers
    • Benefit from visual notice in Maestro of connected LiveDesign servers, user account recall, and automatic connection to LiveDesign with valid single sign-on
  • UI and UX Improvements
    • Independently size column groups and column headers in the LiveReport spreadsheet view
    • View column metadata in tooltip by hovering over a column title in the spreadsheet
    • View the true data point color in Plots after selecting data points
    • Expand and collapse the plot legend to avoid obscuring data points
    • Remove compound images from Plot tooltips
    • Resize columns in the Assay Data Viewer tool
    • Switch between row-per modes more easily
    • Use angle bracket and ampersand characters in formulas for manipulating strings, such as the split() formula
    • Importing compounds through a file and matching by IDs will now skip rows that do not have a match, and report which IDs failed to import

What’s Been Fixed

  • Cell coloring rules no longer extend beyond the edge of a tile
  • Data & Columns tree tooltips show the correct “View” button or “Edit” button for columns, based on each users’ assigned permissions
  • Date and Datetime display formats set within the Admin Panel now apply to all users with a role set to ‘User’
  • Editing a picklist Freeform column value in a Kanban widget will immediately update the tile’s location within the Kanban widget
  • Filter conditions for formula columns that include substructure images will correctly show the substructure images
  • Filter conditions on columns that have file attachments no longer show file IDs in the suggestion dropdown
  • Forms correctly show a pointer cursor instead of a grab cursor when viewing the form
  • Forms now support drilldown from Kanban widgets to Spreadsheet and Table widgets
  • Forms now support multiple instances of the same custom tool
  • Histogram and Pie plots permit creating a defined number of equally distributed bins
  • Histogram plots within Forms now permit defining custom bins
  • LiveDesign will start even if the preprocessor config includes unsupported fields
  • MPO desirability cell borders no longer show a color when the color is defined by a proxy value
  • MPO tooltips now appear in Form widgets that have a drilldown selection
  • Picklist Freeform columns now permit bulk copying dates from assay columns
  • Plots that use the Highlighted Substructure column will now show compound images when defining custom bins in Histogram and Pie charts
  • Plots with a regression line will correctly scale when the plot axes are converted to log scale
  • Plots with log axes no longer show negative values
  • Reagents with numeric IDs will correctly carry through their data when used within Reaction Enumeration
  • Scatter plots with three axes and many data points no longer show blank exports
  • Selecting a range of tiles in a Kanban widget, while holding down the shift key on the keyboard, will now only select the visible tiles within a vertical
  • The maximum number of data points allowed within a plot, set within the Admin Panel, now applies to all users with a role set to ‘User’
  • Toggling to different plot tabs in Forms will now show the correct data point tooltips
  • Typed text within filter conditions, that has not been saved, is now removed after selecting an option from the dropdown list

 

Training & Resources

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Other Resources

Release 2023-1

Library Background

Release Notes

Release 2023-1

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Simultaneously mutate multiple selected nucleotides [2023-1]
  • Quickly select “Nucleic Acids” substructures in the Custom Sets editor dialogs [2023-1]
  • New Custom Presets functionality [2023-1]
    • Option to export subset of only selected presets
    • Expanded control with “Color by Element (Chain Name Carbons)” option
  • Improved “Maestro to PyMOL” capabilities [2023-1]
    • Improved discovery of “Send to PyMOL” option by moving it to the File menu
    • Option to include trajectory data in “Send to PyMOL”
  • Preference to “Keep center of rotation fixed while translating” now enabled by default [2023-1]
  • Project Table / Entry List improvements [2023-1]
    • Automatically scroll to the original row of a Shortcut Row entry with a double-click
    • Select rows where any of the included entries have atoms selected in the Workspace
  • Show/hide pharmacophore feature labels with the Annotations toggle [2023-1]
  • BioLuminate [2023-1]
    • Quickly select antibody-related segments such as CDRs, Fab, Fv and others with predefined selection sets
  • View, analyze and share molecular vibrations information with new Vibrations panel [2023-1]
    • Easy-to-use playback controls and tight interaction with the Workspace
    • Save frames from vibration frequency animation as 3D structures
    • Communicate with colleagues through exported movies of selected modes

Workflows & Pipelining [KNIME Extensions]

  • Support for KNIME (v4.7) added [2023-1]
  • Enhanced robustness of the Extract Properties node where new properties can now be included or excluded [2023-1]
  • The LigPrep node now reads setting files exported from Maestro [2023-1]
  • The new Protein Preparation Workflow node configuration panel is identical to Maestro’s [2023-1]
  • Improvements to LiveDesign Import and Export nodes [2023-1]
    • Move beyond ligands and proteins to import any 2D/3D structure into LiveDesign as generic entities
    • Import entire LiveReports into KNIME for analysis

Target Validation & Structure Enablement

Protein Preparation

  • Small peptides (< 200 atoms) can now also optionally be capped [2023-1]

Multiple Sequence Viewer/Editor

  • Use the Protein Family Alignment feature to align and annotate new sequences to a user supplied reference set for all protein families (beta) [2023-1]
  • Generate complement and reverse complement sequences for Nucleic Acid chains [2023-1]

Desmond Molecular Dynamics

  • Improvement in performance up to 1.17X (17%) in ns/day throughput realized from collaboration with NVIDIA. The largest speed improvements from running on modern GPUs with small to moderate sized systems [2023-1]

QM/MM (Qsite)

  • More robust and reliable minimizations from switching default minimizer from truncated newton to conjugate gradient [2023-1]
  • Added support for dispersion-corrected functionals including DFT-B3LYP-D3, DFT-M06-2X-D3, DFT-B3LYP-D3(BJ), DFT-M06-2X-D3(BJ), DFT-wB97X-D, and DFT-B97-D3 [2023-1]

AutoQSAR

AutoQSAR & DeepAutoQSAR

  • Normalize numeric additional features to eliminate instability in network training in case of very large or small values [2023-1]

Hit Identification & Virtual Screening

Shape Screening

  • Easily restart Shape GPU calculations from the command line [2023-1]

Lead Optimization

FEP+

  • Significant performance improvements of up to 1.4X in FEP+ perturbations/day realized from collaboration with NVIDIA. The greatest speed boosts are observed on larger core count GPUs with small to moderate sized systems [2023-1]
  • Improved interface usability when handling groups, including how values are applied to maps [2023-1]
  • Automatically calculate protomer/tautomer/conformer populations using Epik7 with fep_groups.py script [2023-1]

Quantum Mechanics

  • Easily hide and redisplay spectra in the Spectrum Plot interface [2023-1]
  • Added support for rSCAN, r2SCAN, and r2SCAN-D3(BJ) DFT functionals [2023-1]
  • Added support for thirty-four D4 dispersion corrected functionals [2023-1]

Semi-Empirical Quantum Mechanics

  • Switch from MOPAC7.1 to MOPAC2016 calculation engine for Semiempirical interface [2023-1]

Medicinal Chemistry Design

Ligand Designer

  • Design for ligand selectivity through visualization of binding site volumes accessible to only one of two receptors and identifying ligands that dock well into only one of two receptors (beta) [2023-1]

Biologics Drug Discovery

  • Perform simultaneous back mutations of multiple residues in Antibody Humanization using the CDR grafting workflow [2023-1]
  • Quickly select antibody-related segments such as CDRs, Fab, Fv and others with predefined selection sets [2023-1]
  • Connect data to structure in Residue Scanning where selecting residues in the Workspace now also selects the residues in the Residue Scanning table [2023-1]
  • ‘Crosslink Protein’ interface has been renamed to ‘Protein Linker Design’ to more accurately reflect its purpose. and now includes access to a choice of two loop library databases, one for constructing intradomain linkers and the other for inter-domain linkers [2023-1]

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Quantum ESPRESSO: Improved scalability and versatility of NEB workflow [2023-1]
  • Quantum ESPRESSO: Support for RISM-3D (command line) [2023-1]
  • Quantum ESPRESSO: Dimer method for finding transition states (command line) [2023-1]
  • Quantum ESPRESSO: Option to apply Niggli reduction (command line) [2023-1]

Materials Informatics

Product: MS Informatics

  • Machine Learning Property: Boiling point prediction over a range of pressure [2023-1]
  • Machine Learning Property: Interactive prediction for up to 10 selected entries [2023-1]

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Improved loading speed for a CG system in trajectory viewer [2023-1]
  • CG FF Builder: Warning from *.log for discrepancy in reduced density [2023-1]
  • CG FF Builder: Support for force constants with zero angles [2023-1]
  • CG FF Assignment: Improved UI for setting reduced density and cutoff distance [2023-1]
  • CG FF Assignment: Support for populating reduced density from the FF file [2023-1]
  • CG FF Assignment: Warning for CG particles with large differences in radii [2023-1]

Molecular Dynamics

Product: Desmond

  • Improvement in performance up to 1.17X (17%) in ns/day throughput realized from collaboration with NVIDIA. The largest speed improvements from running on modern GPUs with small to moderate sized systems [2023-1]

MS Maestro User Interface

  • Periodic boundary condition accounted for evaluating “within/beyond” ASL [2023-1]

MS Maestro Builders and Tools

  • Manipulate Cell: Option to shift origin [2023-1]
  • Meta Workflows: Module for building and running multiple connected workflows [2023-1]
  • Polymer: Support for monomers marked by Mark Head and Tail (command line) [2023-1]
  • Query Bonds: Zoom to a bond selected in a row from the panel [2023-1]
  • Structured Liquid: Expanded list of built-in lipids [2023-1]

Classical Mechanics

  • Droplet: Module to predict contact angle of water droplet on a given substrate [2023-1]
  • Evaporation: Workflow module to simulate evaporation with molecular dynamics [2023-1]
  • Molecular Deposition: Allow to request only one MD per iteration [2023-1]
  • Prepare for MD: Option to scale DPD systems based upon the force field [2023-1]
  • Prepare for MD: Additional post-processing for Martini 2.x (command line) [2023-1]

Quantum Mechanics

  • Complex Enumeration: Support for non-metal center in complex stability analysis [2023-1]
  • Macro pKa: Option to assign active atoms in the course of generating tautomers [2023-1]

Education Content

  • New Tutorial: Meta Workflow [2023-1]
  • New Tutorial: Water Droplet Contact Analysis [2023-1]
  • New Tutorial: Liquid Electrolyte Properties: Part 1 [2023-1]
  • New Tutorial: Liquid Electrolyte Properties: Part 2 [2023-1]

LiveDesign

What’s New in 2023-1

  • Project Overview Landing Page: View summarized landing page information
  • Vertical only Kanban: Create a Kanban view using a single picklist Freeform column
  • Forms
    • Use keyboard controls with the Compound Image widget to navigate from one compound to another
    • Define independent column widths for each widget
  • Generic Entity: Store Lot and Experimental data on any chemical, biological, or material matter with any representation
  • New enhancements to existing features, such as:
    • View the regression equation on scatter plots
    • Admins can log out users by removing their assigned Roles, or by specifying a specific username
    • Removing a single enhanced stereochemical AND group with the structure processor now defines the stereocenter as ‘undefined’, and previously would define is as an absolute stereocenter
    • Use advanced atom query features in substructure filtering
  • Performance Improvements
    • SAR analysis tasks are performed asynchronously and take less time to complete
    • The 3D visualizer is more responsive when large proteins are viewed

What’s Been Fixed

  • Models that returned a single value now show the full result unaligned with other columns
  • The 3D visualizer uses the high performance GPU on client computers to avoid crashes
  • Pasting multiple values into ID search will attempt to automatically identify the delimiter, or present an option to select the delimiter, by which to separate values
  • Limited Assay Columns are now included in Matched Molecular Pairs analyses
  • Pasting multiple values into Filters will attempt to automatically identify the delimiter, or present an option to select the delimiter, by which to separate values
  • File import receipts show a scroll bar for errors parsing SD files
  • Pinned plot tooltips in forms update the connecting line when the view is resized
  • The file import receipt now shows IDs from SD file title lines
  • Overlay lines in plots will now extend to the full plot dimensions
  • Automatically generated coloring rules for R-groups, Scaffolds, and String columns that were created through the plot interface will now appear in plots
  • Pinned tooltips now point to the correct data points in box plots
  • The radar chart legend now shows which compound IDs are out of range by hovering over the “Out of Range” entry in the plot legend
  • Multi-parameter optimization column dialogs appeared to allow unauthorized users to edit the definition, however any edits submitted were not saved. The dialog now shows all fields as disabled to unauthorized users.
  • LiveReports set to Read-Only no longer allow users to configure Tile View
  • Line charts no longer show R-groups as SMILES strings in the plot legend, and instead show images
  • Forms with many narrow widgets not longer show errors when editing the Form
  • Dragging to select multiple histogram bars will select all of the compounds within the bin

Training & Resources

Online Certification Courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Other Resources

Release 2022-4

Library Background

Release Notes

Release 2022-4

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Create and share custom visualization Presets  [2022-4]
  • Added support to mutate DNA/RNA to standard nucleobases  [2022-4]
  • New Workflow Action Menu to guide to next steps for Plot Rigid/Relaxed scans [2022-4]
  • First full release of the new 2D sketcher [2022-4]
  • Get Going with Maestro Video Series added to Documentation [2022-4]

Workflows & Pipelining [KNIME Extensions]

  • Includes the latest version of KNIME (v4.6.1) [2022-4]
  • The number of matches can now be controlled in the Phase screening node [2022-4]

In LiveDesign:

  • When deploying a model the suitable KNIME protocol is chosen automatically and the latest version of the protocol uploaded [2022-4]
  • Distribution of calculations is controlled from the model admin page [2022-4]
  • Model changes from the LiveDesign Admin page can be preserved when overwriting an existing model [2022-4]
  • A new administration node to move, archive and unarchive models [2022-4]

Target Validation & Structure Enablement

Protein Preparation

  • Significant speedup when opening the Protein Preparation Workflow interface on Windows [2022-4]
  • Reduced verbosity of Protein Preparation Workflow log file by limiting irrelevant CCD bond assignment error notices [2022-4]
  • Protein Reliability Report will generate TEST reflections on-the-fly, if not available in provided .cv file, and report RSCC values [2022-4]
  • Updated PROPKA to (latest) version 3.4 [2022-4]

Protein X-Ray Refinement

  • Introduction of GlideXtal command line tool for automatic ligand fitting in crystallographic electron density maps [2022-4]
  • PrimeX minimization is able to use structure factors in CIF format [2022-4]
  • In Phenix/OPLS can now remove all entities clashing with crystal mates [2022-4]
  • Phenix/OPLS is more robust to missing atoms in standard residues [2022-4]

Cryo-EM Model Refinement

  • Beta GlideEM interface for ligand placement into cryo-electron density maps [2022-4]
  • GlideEM now accepts gzipped (CCP4, MRC, MAP) files as input [2022-4]

Multiple Sequence Viewer/Editor

  • Beta release of Protein Family Alignment and Annotation [2022-4]
    • A new category named ‘Family Feature Calculation’ located in the ‘Other Tasks’
    • Menu exposes protein family alignment and annotation
    • Supports kinase and GPCR Alignments
    • Supports annotation of GPCR regions
  • Dendrogram hover tooltip to display distance information [2022-4]

IFD-MD

  • Membrane-bound IFD-MD tutorial [2022-4]
  • Covalent ligand IFD-MD tutorial [2022-4]

FEP+

  • Show user-friendly message when undefined stereochemical centers are introduced [2022-4]
  • Improved usability of FEP+ group panel to manage protonation and tautomeric states ensemble – for more accurate ΔΔG predictions [2022-4]

Constant pH Simulations (Beta)

  • Improved usability of constant pH simulations for protein pKa calculations with friendly outputs [2022-4]

AutoQSAR

  • Added MACCs keys for ligand featurization in DeepAutoQSAR [2022-4]
  • Include ElasticNetCV model (strongly l1/l2 regularized linear regression) in DeepAutoQSAR hyper-parameter optimization [2022-4]
  • New DeepAutoQSAR command line utility for greater ease-of-use [2022-4]

Desmond Molecular Dynamics

  • In Trajectory Plots view Ramachandran plot of Protein Residues [2022-4]

Empirical and QM-based pKa Prediction

  • Initial release of Epik 7, a new machine learning based application for fast pKa value and protonation state prediction [2022-4]
    • Epik 7 can also produce a plot giving the populations of states as a function of pH

Solubility FEP (Beta)

  • Option to show solubility results in logS unit [2022-4]

Quantum Mechanics

  • Calculate ESP charges for excited states under the TDDFT/TDA approximation [2022-4]
  • Complete calculations faster with parallel Jaguar calculations on Windows [2022-4]
  • New Workflow Action Menu to guide to next steps for Plot Rigid/Relaxed scans [2022-4]
  • Over 80 examples of Jaguar input files in documentation [2022-4]

Semi-Empirical Quantum Mechanics

  • GFN2-xTB method now available in the Semiempirical Module panel [2022-4]

Biologics Drug Discovery

  • To improve protein linker design, the loop database was updated to a new version specifically intended for interdomain linker design [2022-4]
  • To expand chemical liability detection, Asp isomerization pattern and free cysteine detection were added to the Reactive Residues interface [2022-4]
  • New command-line script for running Protein Interaction Analysis with the ability to export results to csv format [2022-4]
  • First full release of Protein Descriptors interface which now supports .mae and .maegz files containing multiple structures [2022-4]
  • Copy-paste sequences into Antibody Structure Prediction interface under new “Enter new sequence” option [2022-4]
  • Reuse the same input csv file format for batch homology modeling in Antibody Structure Prediction when running from the command line or Maestro [2022-4]
  • Added support for ‘keep glycan’ option in Antibody Structure Prediction during batch modeling [2022-4]
  • Cysteine scanning panel for disulfide design now supports remote job submission which is useful for running large jobs e.g. using when MD trajectory as input [2022-4]
  • Added “Antibody-Antigen” to Interactions scope dropdown [2022-4]
  • Get Going with BioLuminate Video Series added to Documentation [2022-4]

Materials Science

GUI for Quantum ESPRESSO

  • Quantum ESPRESSO GUI: Option to hide selected atoms [2022-4]
  • Quantum ESPRESSO GUI: Upgraded NEB UI for improved UX [2022-4]
  • Quantum ESPRESSO: Endpoints saved for NEB mae files at each iteration [2022-4]
  • Quantum ESPRESSO: Reduced file size for custom saved NEB setups [2022-4]
  • Quantum ESPRESSO: Use of automatic parallelization with GUI support [2022-4]
  • Quantum ESPRESSO: -save_failures option for driver (command line) [2022-4]
  • Quantum ESPRESSO: -last_only option for qe2mae tool to save the final structure only  (command line) [2022-4]
  • Quantum ESPRESSO: HUBBARD options enabled in input *.cfg (command line) [2022-4]
  • Quantum ESPRESSO: Automatic restart for long AIMD simulations [2022-4]
  • Quantum ESPRESSO: Support for cell volume relaxation [2022-4]

Molecular Dynamics

  • Viscosity: ”,” used as the delimiter in CSV output for Einstein-Helfand analysis [2022-4]

Materials Informatics

  • Machine Learning Property: Pre-built, validated machine learning models for a selective list of materials properties [2022-4]
  • Molecular Descriptors: Report of semiempirical HOMO-LUMO gap [2022-4]
  • Molecular Descriptors: Support for plotting molecular orbitals [2022-4]

Coarse-Grained (CG) Molecular Dynamics

  • Support for Ewald sums with Martini force field [2022-4]
  • CG FF Builder: Option to export the viewer data to CSV [2022-4]
  • CG FF Builder: Support for the use of existing trajectory [2022-4]
  • Map Atoms to Particles: Option to map selected atoms from the input structure [2022-4]

Penetrant Loading Simulation

  • Penetrant Loading: Robust handling of GCMC water models [2022-4]
  • Penetrant Loading: Visualization of periodic unit cell for the output structures [2022-4]

MS Maestro User Interface

  • Resized Trajectory Analysis task frame in Maestro for better user experience [2022-4]

MS Maestro Builders and Tools

  • Complex Builder: Expansion of ligand library [2022-4]
  • Disordered System: Option to generate cells with different numbers of molecules [2022-4]
  • Elemental Enumeration: Jobs launched to queue instead of running interactively [2022-4]
  • Manipulate Cell: Option to translate within -0.5 and 0.5 of the fractional coordinate [2022-4]
  • Manipulate Cell: Support for change of lattice dimensions without FF retyping [2022-4]
  • Query Bonds: Option to export output to CSV [2022-4]
  • Semicrystalline Polymer: Option to use existing crystal (command line) [2022-4]
  • Semicrystalline Polymer: Reporting percentage of crystallinity [2022-4]
  • Semicrystalline Polymer: Speed-up for building with polymer models [2022-4]

Classical Mechanics

  • MD Multistage: Improved estimation of timestep for Martini systems [2022-4]
  • MD Multistage: Option to concatenate stages together for speed-up [2022-4]
  • MD Multistage: Built-in Martini relaxation protocol suitable for NVT ensemble [2022-4]
  • MD Multistage: Option to only write out selected molecules to trajectories (command line) [2022-4]
  • Stress Strain: Output CSV updated at each new data point [2022-4]
  • Thermophysical Properties: Support for Parrinello-Rahman barostat (command line) [2022-4]

Quantum Mechanics

  • Band Shape: Option to add/select implicit solvent [2022-4]
  • Organometallic Conformational Search: Option to select conformers after QM calculations (command line) [2022-4]
  • Organometallic Conformational Search: Support for custom MacroModel COM files (command line) [2022-4]
  • Organometallic Conformational Search: Support for MOPAC (command line) [2022-4]

Education Content

  • Get Going with Materials Science Maestro Video Series added to Documentation [2022-4]
  • Quick Reference Sheets available from both Documentation and Training webpages [2022-4]
  • New Tutorial: Evaporation [2022-4]
  • New Tutorial: Machine Learning Property Prediction [2022-4]
  • Updated Tutorial: Polymer Electrolyte Analysis [2022-4]
  • Updated Tutorial: Computing Atomic Charges [2022-4]
  • Updated Tutorial: Activation Energies for Reactivity in Solids and on Surfaces [2022-4]
  • Updated Tutorial: Organometallic Complexes [2022-4]

LiveDesign

What’s new in LiveDesign 2022-4

  • The number of logins is enforced by a license limit: Users who attempt to log in after the number of available seats have been assigned will be denied access to LiveDesign
  • Admins can forcibly log out users: Users can be forcibly logged out by removing their assigned Roles, or by specifying a specific username
  • File Import Receipt: Receive feedback on file imports when the file contains errors, and instructions on how to correct the errors
  • Sketcher Improvements:
    • Implicit mode sketcher: Quickly switch between editing compounds and selecting a subset of the compound, by clicking directly on the select tools and draw tools
    • Delete an atom by hovering over it and pressing the Backspace key on the keyboard
  • Setting a protocol’s parameter to “Set Default” in the admin panel changes all existing models’ parameter to “Set Fixed”
  • Interaction Surface within LigandDesigner: view the interaction surface to determine available growth space within a binding pocket
  • Copying a single compound from the spreadsheet can be copied in a Mol v3000 format: a server wide setting permits copying a single molecule as either Extended SMILES or Mol v3000. Selecting and copying multiple compounds as once will copy the compounds using a SMILES format
  • Freeform column picklist options can be reordered: Editing a Freeform column definition permits reordering the picklist options
  • UI Improvements
    • View more tiles on screen in Tile View, which has a much smaller tile size limit
    • View more data in a spreadsheet cell; the “More Available…” message within spreadsheet cells has been replaced with a gradient to indicate additional data is in the cell
    • Users can use Ctrl-Click to easily display results from additional 3D models in the 3D visualizer.
    • Exported compound structure images will now include stereochemistry labels if these are turned on in the spreadsheet.

What’s Been Fixed

  • Model columns can now be sorted and filtered when the cell contains both blank values and numeric or string values. Sorting will use the first non-empty value in the cell.
  • Advanced searches with multiple Freeform column conditions return the same results even if the condition order is changed
  • Compound images no longer show large atom labels when clicking on them within the main spreadsheet
  • Project admins can edit all formulas within their projects
  • Pinned plot tooltips in the visualize panel will reappear even after switching to another plot or another LiveReport
  • Deleted LiveReports cannot be reopened by navigating directly to the LiveReport’s URL
  • Pinned plot tooltips in forms update the connecting line when the view is resized
  • Pasting multiple values into Filters will, once again, attempt to automatically identify the delimiter, or present an option to select the delimiter, by which to separate values.
  • The LiveReport Manager dialog no longer obscures the last LiveReport with a horizontal scroll bar
  • Bond angles for attachment points and carbon atoms from R-group decompositions are now displayed as angles of less than 180 degrees, while before they were displayed at a 180 degree angle
  • LiveReports with 3D model returns no longer show a red error bar after opening
  • Commons-text has been upgraded to patch security vulnerability CVE-2022-42889
  • The 3D visualizer uses the high performance GPU on client computers to avoid crashes
  • Formula results no longer disappear from the spreadsheet when columns used in the formula are hidden in the LiveReport
  • The matched molecular pairs tool no longer fails to parse chiral compounds represented in an Extended SMILES format
  • Error messages no longer sporadically appear when models are updated and saved in the Admin Panel

Training & Resources

Online Certification Courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Other Resources

Release 2022-3

Library Background

Release Notes

Release 2022-3

Small Molecule Drug Discovery

Target Validation & Structure Enablement

Protein Preparation

  • Reorganized and grouped Protein Prepwizard command line options for improved clarity and correspondence with the Maestro interface [2022-3]
  • Improved valence error reporting in Diagnostics interface [2022-3]
  • Several orders of magnitude speedup when assigning zero-order bonds and running the Epik stage in the Protein Prepwizard on large structures such as a ribosome [2022-3]
  • Several times faster performance of ProtAssign when running on large structures with many small clusters or structures with a few large clusters [2022-3]
  • Addition of -include_ligand_states flag in command line prepwizard and protassign scripts, to include Epik generated ligand states during the hydrogen bond assignment stage [2022-3]

Protein X-Ray Refinement

  • Phenix/OPLS: Option to significantly improve computational performance by lowering the nonbonded energy term cutoff [2022-3]
    • A Phenix-side option schrodinger.flags.nonbonded_cutoff changes the cutoff. Default is to not change the cutoff. This has been tested with a cutoff of 10 angstrom, which increases computational efficiency by several times while no penalty to refinement statistics is observed.

Cryo-EM Model Refinement

  • Introduced a new mode peptide for GlideEM, for enhanced peptide sampling with the command line parameter -nconformers specifying the number of conformers to generate [2022-3]
    • Additional input conformations are generated by running confgen on the peptide and redocking each conformation. The time and required computational resources required scale linearly with -nconformers
  • Binding pocket / docking grid center can now be specified by binding site ASL and the ligand provided in a separate file in GlideEM [2022-3]
    • The binding site can be specified using the new -binding_site_asl command line argument, which requires an ASL that specifies residues near the binding site. The docking grid center will be the geometric mean of all atoms specified by the ASL. The ligand can be provided using the -ligand_struct command line argument

Platform Environment

Maestro Graphical Interface

  • Apply styling and change molecular representations on selected entries [2022-3]
  • New Workflow Action Menu support for Protein-Protein Docking [2022-3]
  • “Send to PyMOL” panel preserves Maestro’s non-bonded interactions when viewed in PyMOL [2022-3]
  • Create zero order bonds withing the 2D sketcher [2022-3]
  • More reliable selection with improved accuracy of selecting atoms and bonds within the 2D sketcher [2022-3]
  • Save user selection of authentication with LiveDesign by either credentials or single sign-on [2022-3]
  • New Help icon provides access to relevant tutorials as well as documentation [2022-3]

Force Field

  • Improvements to scalability of large FFBuilder jobs [2022-3]

Workflows & Pipelining [KNIME Extensions]

  • Schrödinger extensions are compatible with KNIME 4.6 [2022-3]
  • Create and apply ML models with new DeepAutoQSAR nodes [2022-3]

Hit Identification & Virtual Screening

ABFEP

  • Performance in ABFEP loading [2022-3]

Lead Optimization

FEP+

  • Drastically improved interactive performance of FEP+ Analysis tab interface with large maps with 100s of nodes edges [2022-3]
  • Correlation plots will show pairwise ddG histogram (previously edgewise was shown) [2022-3]
  • State Groupings GUI [2022-3]
    • Tautomers
    • Protomers
    • Conformers (binding poses)

Protein FEP

  • Residue Mutation Selection layout change [2022-3]
    • Added support of CYM amino acid (deprotonated Cysteine)

Constant pH Simulations (Beta)

  • The pH interval is fixed at 0.5 units and show number of resulting replicas [2022-3]

Solubility FEP

  • Experimental ΔG data is shown in Analysis tab if the data is available [2022-3]

Biologics Drug Discovery

  • Protein Interaction Analysis
    • Filter protein-protein interaction by residue features, non-bonded interaction types, and interaction distances [2022-3]
    • Buried solvent-accessible surface area and surface complementarity of interface residues reported in results table [2022-3]
    • Select, display and style only interface residues in interaction analysis panel [2022-3]
  • Residue Mutation
    • Enhanced performance of residue scanning from Maestro, enabled hundreds of thousands of mutations to be examined simultaneously [2022-3]
    • Mutated residue name added as as property in the residue scanning output structure to facilitate downstream analysis and workflow scripting [2022-3]
  • Protein-Protein Docking:
    • Guidance on common next steps provided following PIPER docking through the Workflow Action Menus  [2022-3]
  • Antibody Loop Modeling
    • Specify numbering scheme, and thus loop definitions, prior to running PRIME loop refinement including Chothia, Enhanced Chothia, Kabat, IMGT and AHo [2022-3]

Materials Science

GUI for Quantum ESPRESSO

  • Effective Screening Medium: Option to align structures in GUI from selected entries [2022-3]
  • Quantum ESPRESSO: Support for runner.py to run TDDFPT [2022-3]
  • Quantum ESPRESSO: Restart option for ab initio MD (command line) [2022-3]
  • Quantum ESPRESSO: Support for slab models with custom dimensionality [2022-3]
  • Quantum ESPRESSO: Upgrade to Quantum ESPRESSO 7.1 [2022-3]

Molecular Dynamics

Transport Calculations via MD simulations

  • Viscosity: Thermostat and barostat settings (command line) [2022-3]

Coarse-Grained (CG) Molecular Dynamics

  • CGFF Builder: Better estimation of particle volume prediction using atomistic structures [2022-3]
  • CGFF Builder: Option to set common mass for all particles. [2022-3]
  • CGFF Builder: Implicit charges stored in the FF file and reported in the viewer [2022-3]
  • CGFF Builder: Option to import saved SMARTS pattern [2022-3]
  • CGFF Builder: Default bond-length bounds adjusted by the cutoff [2022-3]
  • CGFF Builder: Each CG-mapped molecule type saved as a copy [2022-3]
  • Viscosity: Automatic setup of thermostat and barostat for CG systems [2022-3]

Optoelectronics

  • AL OptoE: Expanded property space for optimization [2022-3]

Dielectric Properties

  • Complex Permittivity: Improved UI to show permittivity for specified frequency [2022-3]
  • Complex Permittivity: Separate visualization of storage and loss functions [2022-3]

MS Maestro Builders and Tools

  • Nanostructure: Periodicity of the output structures set by default [2022-3]
  • Polymer: Template for Chitosan and Xanthan Gum under Carbohydrates [2022-3]
  • Polymer: Hydroxyl group as the default terminator for carbohydrates [2022-3]
  • Semicrystalline Polymer: Support for running on multiple hosts [2022-3]

Classical Mechanics

  • Molecular Deposition: Preview of the number of MD stages [2022-3]
  • MD Multistage: Temperature control in brownian stage [2022-3]
  • MD Multistage: Relaxation protocol for stiff polymers [2022-3]
  • Polymer Crosslink: Option to store and recall SMARTS patterns [2022-3]
  • Polymer Crosslink: Option for SMARTS search method (command line) [2022-3]
  • Stress Strain: Option for SMARTS search method (command line) [2022-3]
  • Stress Strain: Speed up (up to 2x) of cyclic stress strain jobs [2022-3]
  • Stress Strain: Preview of total simulation time from the GUI [2022-3]
  • Surface Tension: Support for long-range cut-off [2022-3]
  • Surfactant Tilt: Improved UI for surfactant selection [2022-3]
  • Trajectory Density Analysis: Improved UI for trajectory range setup [2022-3]

Quantum Mechanics

  • Adsorption Enumeration: Option to position adsorbate distanced from the substrate [2022-3]
  • Excited State Analysis: Support for custom definition of fragment [2022-3]
  • QM Multistage: Option to turn off robust convergence [2022-3]
  • Ligand Exchange: Report of detailed progress in the driver log [2022-3]
  • Automatic spin treatment as default for Jaguar Options within QM panels [2022-3]
  • Reaction Workflow: Support for geometry deduplication [2022-3]
  • Reaction Workflow: Support for η- or centroid- representation for output [2022-3]
  • Reaction Workflow: Support for specifying R-group enumeration sites [2022-3]

LiveDesign

What’s new in LiveDesign 2022-3

  • Machine Learning: Predict properties by building and using DeepAutoQSAR and a model management tool
  • Kanban layouts: Visualize and manage workflows, projects, and synthesis queues
  • View all assay data for a compound: Query all assay data for a compound by using the Assay Data Viewer tool
  • Matched Molecular Pairs: Perform a matched molecular pairs analysis by querying precomputed datasets or generating analyses on-the-fly, and analyze multiple properties at once
  • New enhancements to existing features, such as:
    • Configure models to use options from a picklist
    • Plot multiple experimental values for a single compound
    • Updated plot legend that no longer overlays the plot
    • View drop down suggestions that are specific to the LiveReport in Filters, Coloring Rules dialogs, and MPO Configuration dialogs
    • Switch modes in the Sketcher by using the keyboard’s spacebar
    • Ligand Designer allows for more simultaneous users due to more rapid license checkout and return
    • Compound images show heteroatoms that are proportional to bond lengths
    • Hide tabs on Forms widgets to improve screen real estate usage
    • Updated tooltips for experimental values, which can open the Assay Viewer tool to inspect experimental metadata
    • Automatically generate coloring rules based on row selection for categorical columns
  • New performance improvements that speed up advanced search, speed up sorting, and speed scrolling in LiveReports with many columns
  • Workflows & Pipelining [KNIME Extensions]
    • When deploying a model the suitable KNIME protocol is chosen automatically and the latest version of the protocol uploaded
    • Distribution of calculations is controlled from the model admin page
    • Model changes from the LiveDesign Admin page can be preserved when overwriting an existing model
    • A new administration node to move, archive and unarchive models

What’s been fixed

  • Column tree search terms and results are now resetting upon clicking cancel on a dialog and accessing again.
  • Tile View header names  for unpublished columns use a hash background for the entire cell, while before it was only used for the text itself.
  • A format inconsistency when a Tile includes a 3D column has been fixed.
  • The “Unfreeze all rows” message now disappears once clicked from other views than spreadsheet, while before it was persisting.
  • Global templates can now be applied to an existing LiveReport.
  • The display of the tooltip of the Save button for Landing Page bookmarks has been fixed.
  • In the LiveReport picker, selection is now kept if the creation of a new folder is aborted.
  • LiveReports including MPO can now be copied to other projects.
  • Project picker does not persist anymore if a user navigates to a LiveReport using its URL.
  • Date field values of Landing Page bookmarks are now validated. An incorrect value will trigger a warning message.
  • Template search on Landing Page is now case insensitive.
  • MPO tooltip now properly displays all constituents information even for constituents not present in the current view.
  • MPO tooltip position was not consistent depending on the presence of a proxy value score. It now is.
  • MPO score now appropriately reflects the change of a previously defined constituent that is changed to a null value.
  • Legend now pops out with a chart when not attached.
  • Plot legend is now appropriately repositioned upon screen resolution changes.
  • Box Plots now supports a larger number of data points.
  • Export to image of line plots split by series with a large amount of data now include all data points as expected.
  • Histogram and Pie plot tooltips now show decimal separators as per the server setting.
  • In the Firefox browser, user axis font size is now taken in account while it was ignored before.
  • The warning message indicating that the number of allowed points in a plot has been exceeded is now properly displayed. In particular it is not hidden anymore by the message offering to activate Jitter functionality in scatter plots.
  • When exporting plots to images, data points now have borders, preventing points without color to not be displayed in the image.
  • Sgroup annotations of abbreviated functional groups for structure imported in v2000 CTAB format are now properly displayed.
  • The property RDKIT_STRUCTURE_PROCESSOR_SGROUP_FIELD_NAMES, which defines which Sgroup fields should be considered as part of registration, is now properly taken in account.
  • R-group Decomposition now matches tautomers when “RDKIT_TAUTOMER_SEARCH” is set to true.
  • First click on the sketcher now focuses the input, while before it was adding a CH4 to the sketch.
  • Fix an issue impacting the sketcher display after successive change of the size and/or minimization of the browser window.
  • Upon upgrade, the new version of the sketcher does not require a hard refresh of the page orto clear the browser cache.
  • Dragging a compound into the sketcher, and then adding a ring or bond, will show a proportionally sized ring or bond.
  • Advanced searches with an inverted “Presence in LiveReport” now return results.
  • Freeform columns that are hidden in the LiveReport now appear as options for the Kanban widget.
  • The kanban widget now shows tiles in edit mode after configuring the widget.
  • R-groups can now be added to the LiveReport via the sketcher.
  • Uploading unpublished data from Maestro, and then re-uploading that data as published to the same LiveReport, now shows those columns in the Data & Columns tree.
  • Hidden columns now appear in the file export when they are explicitly selected.

Training & Resources

Online Certification Courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Other Resources

Citations

Citations

BioLuminate®

  • Sankar, K.; Trainor, K.; Blazer, L.; Adams, J.; Sidhu, S.; Day, T.; Meiering, E.; Maier, J., “A descriptor set for quantitative structure-property relationship prediction in biologics”, Mol Inform, 2022, 41(9), 2100240
  • Tavella, D.; Ouellette, D. R.; Garofalo, R.; Zhu, K.; Xu, J.; Oloo, E. O.; Negron, C.; Ihnat, P.M., “A novel method for in silico assessment of Methionine oxidation risk in monoclonal antibodies: Improvement over the 2-shell model”, PLoS One, 2022, 17(12)
  • Sankar, K.; Krystek, S. R. Jr; Carl, S. M.; Day, T.; Maier, J. K. X., “AggScore: prediction of aggregation-prone regions in proteins based on the distribution of surface patches”, Proteins, 2018, 86(11), 1147-1156
  • Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D., “Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction”, Proteins, 2014, 82(8), 1646-1655
  • Salam, N. K.; Adzhigirey, M.; Sherman, W.; Pearlman, D. A., “Structure-based approach to the prediction of disulfide bonds in proteins”, Protein Eng Des Sel, 2014, 27(10), 365-74
  • Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K. A., “Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes”, PLoS ONE, 2013, 8(12), e82849

Schrödinger Release 2026-1: BioLuminate, Schrödinger, LLC, New York, NY, 2025.

 


 

Canvas

  • Duan, J.; Dixon, S. L.; Lowrie, J. F.; Sherman, W., “Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods”, J. Molec. Graph. Model., 2010, 29, 157-170
  • Sastry, M.; Lowrie, J. F.; Dixon, S. L.; Sherman, W., “Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments”, J. Chem. Inf. Model., 2010, 50, 771–784

Schrödinger Release 2026-1: Canvas, Schrödinger, LLC, New York, NY, 2025.

 


 

ConfGen

Schrödinger Release 2026-1: ConfGen, Schrödinger, LLC, New York, NY, 2025.

 


 

Core Hopping

Schrödinger Release 2026-1: Core Hopping, Schrödinger, LLC, New York, NY, 2025.

 


 

CovDock

  • Zhu, K.; Borrelli, K. W.; Greenwood, J. R.; Day, T.; Abel, R.; Farid, R. S.; Harder, E., “Docking covalent inhibitors: A parameter free approach to pose prediction and scoring”, J. Chem. Inf. Model., 2014, 54, 1932−1940

Schrödinger Release 2026-1: CovDock, Schrödinger, LLC, New York, NY, 2025.
 


 

DeepAutoQSAR

  • Kaplan, Z.; Ehrlich, S.; Leswing, K., “Benchmark study of DeepAutoQSAR, ChemProp, and DeepPurpose on the ADMET subset of the Therapeutic Data Commons”, Full Article
  • Gion, K.; Gattani, S.; Kaplan, Z., “DeepAutoQSAR hardware benchmark”, Full Article

Schrödinger Release 2026-1: DeepAutoQSAR, Schrödinger, LLC, New York, NY, 2025.
 


 

Desmond

Schrödinger Release 2026-1: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2024. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2025.

 


 

Epik

  • ​​Johnston, R. C.; Yao, K.; Kaplan, Z.; Chelliah, M.; Leswing, K.; Seekins, S.; Watts, S.; Calkins, D.; Chief Elk, J.; Jerome, S. V.; Repasky, M. P;. Shelley, J. C., “Epik: pKa and protonation state prediction through machine learning”, J. Chem. Theory Comput. 2023, 19, 2380–2388

Schrödinger Release 2026-1: Epik, Schrödinger, LLC, New York, NY, 2025.

Please note that the pKa and tautomeric databases provided with Epik are copyrighted material, and should not be extracted, reproduced, or used outside of the context of Epik or LigPrep licensed calculations.

 


 

FEP+

  • Ross, G. A., Lu, C., Scarabelli, G.; Albanese, S. K.; Houang, E.; Abel, R.; Harder, E. D.; Wang, L., “The maximal and current accuracy of rigorous protein-ligand binding free energy calculations”, Commun. Chem., 2023, 6(222)
  • Chen, W.; Cui, D.; Jerome, S.; Michino, M.; Lenselink, E.; Huggins, D.; Beautrait, A.; Vendome, A.; Abel, R.; Friesner, R. A.; Wang, L., “Enhancing hit discovery in virtual screening through absolute protein–ligand binding free-energy calculations”, J. Chem. Inf. Model., 2023, 63(10), 3171–3185
  • Abel, R.; Wang, L.; Harder, E. D.; Berne, B. J.; Friesner, R. A., “Advancing drug discovery through enhanced free energy calculations”, Acc. Chem. Res., 2017, 50(7), 1625-1632
  • Kuhn, B.; Tichý, M.; Wang, L.; Robinson, S.; Martin, R. E.; Kuglstatter, A.; Benz, J.; Giroud, M., Schirmeister, T.; Abel, R.; Diederich, F.; Hert, J., “Prospective evaluation of free energy calculations for the prioritization of Cathepsin L Inhibitors”, J. Med. Chem., 2017, 60(6), 2485-2497
  • Yu, H. S.; Deng, Y.; Wu, Y.; Sindhikara, D.; Rask, A. R.; Kimura, T.; Abel, R.; Wang, L., “Accurate and reliable prediction of the binding affinities of macrocycles to their protein targets”, J. Chem Theory Comput., 2017, 13(12), 6290-6300
  • Wang, L.; Deng, Y.; Wu, Y.; Kim, B.; LeBard, D. N.; Wandschneider, D.; Beachy, M.; Friesner, R. A.; Abel, R., “Accurate modeling of scaffold hopping transformations in drug discovery”, J. Chem Theory Comput., 2017, 13(1), 42-54
  • Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A., “OPLS3: A force field providing broad coverage of drug-like small molecules and proteins”, J. Chem. Theory Comput., 2016, 12(1), 281–296
  • Wang, L.; Wu, Y.; Deng, D.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. , “Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field”, J. Am. Chem. Soc., 2015, 137(7), 2695–2703

Schrödinger Release 2026-1: FEP+, Schrödinger, LLC, New York, NY, 2025.

 


 

Force Fields

  • Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G. A.; Dahlgren, M. K.; Russell, E.; Von Bargen, C. D.; Abel, R.; Friesner, R. A.; Harder, E. D., “OPLS4: Improving force field accuracy on challenging regimes of chemical space”, J. Chem. Theory Comput., 2021, 17(7), 4291–4300
  • Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder E. D., “OPLS3e: Extending force field coverage for drug-like small molecules”, J. Chem. Theory Comput., 2015, 15(3), 1863–1874
  • Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A., “OPLS3: A force field providing broad coverage of drug-like small molecules and proteins”, J. Chem. Theory Comput., 2016, 12(1), 281–296
  • Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., “Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field”, J. Chem. Theory Comput., 2010, 6, 1509–1519
  • Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J., “Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids”, J. Am. Chem. Soc., 1996, 118 (45), 11225-11236
  • Jorgensen, W. L.; Tirado-Rives, J., “The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin”, J. Am. Chem. Soc., 1988, 110(6), 1657-1666

Schrödinger Release 2026-1: Force Fields, Schrödinger, LLC, New York, NY, 2025.
 


 

Formulation ML

  • Chew, A.K.; Afzal, M.A.F.; Kaplan, Z.; Collins, E. M.; Gattani, S.; Misra, M.; Chandrasekaran, A.; Leswing, K.; Halls, M.D., “Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures”, npj Comput Mater, 2025, 11(72)

Schrödinger Release 2026-1: Formulation ML, Schrödinger, LLC, New York, NY, 2025.
 


 

Glide

  • Yang, Y; Yao, K; Repasky, M. P.; Leswing, K; Abel, R; Shoichet, B. K.; Jerome, S. V., “Efficient exploration of chemical space with docking and deep learning”, J. Chem. Theory Comput. 2021, 17(11), 7106–7119
  • Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T., “Extra precision Glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes”, J. Med. Chem., 2006, 49, 6177–6196
  • Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L., “Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening”, J. Med. Chem., 2004, 47, 1750–1759
  • Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S., “Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy”, J. Med. Chem., 2004, 47, 1739–1749

Schrödinger Release 2026-1: Glide, Schrödinger, LLC, New York, NY, 2025.

 


 

GlideEM

  • Robertson, M. J.; van Zundert, G. C. P.; Borrelli, K.; Skiniotis, G., “GemSpot: A Pipeline for Robust Modeling of Ligands into CryoEM Maps”, Structure., 2020, 28(6), 707-716

Schrödinger Release 2026-1: GlideEM, Schrödinger, LLC, New York, NY, 2025.

 


 

IFD-MD

  • Miller, E. B.; Murphy, R. B.; Sindhikara, D.; Borrelli, K. W.; Grisewood, M. J.; Ranalli, F., Dixon; S. L., Jerome; S., Boyles, N. A.; Day, T.; Ghanakota, P.; Mondal, S.; Rafi, S. B.; Troast, D. M.; Abel, R.; Friesner, R. A., “Reliable and accurate solution to the induced fit docking problem for protein–ligand binding”, J. Chem. Theory Comput. 2021, 17(4), 2630–2639
  • Xu, T., Zhu, K.; Beautrait, A.; Vendome, J.; Borrelli, K. W.; Abel, R.; Friesner, R. A.; Miller, E. B., “Induced-fit docking enables accurate free energy perturbation calculations in homology models”, J. Chem. Theory Comput. 2022, 18(9), 5710–5724
  • Coskun, D.; Lihan, M.; Rodrigues, J. P. G. L. M.; Vass, M.; Robinson, D.; Friesner, R. A.; Miller, E. B., “Using AlphaFold and experimental structures for the prediction of the structure and binding affinities of GPCR complexes via induced fit docking and free energy perturbation”, J. Chem. Theory Comput. 2023

Schrödinger Release 2026-1: IFD-MD, Schrödinger, LLC, New York, NY, 2025.
 


 

Induced Fit (IFD)

Schrödinger Release 2026-1: Induced Fit Docking protocol; Glide, Schrödinger, LLC, New York, NY, 2024; Prime, Schrödinger, LLC, New York, NY, 2025.

 


 

Jaguar

  • Cao, Y.; Balduf, T.; Beachy, M. D.; Bennett, M. C.; Bochevarov, A. D.; Chien, A; Dub, P. A.; Dyall, K. G.; Furness, J. W.; Halls, M. D.; Hughes, T. F.; Jacobson, L. D.; Kwak, H. S.; Levine, D. S.; Mainz, D. T.; Moore, K. B.; Svensson, M; Videla, P. E.; Watson, M. A.; Friesner R. A., “Quantum chemical package Jaguar: A survey of recent developments and unique features”, J. Chem. Phys. 2024, 161(5), 052502
  • Bochevarov, A. D.; Harder, E.; Hughes, T. F.; Greenwood, J. R.; Braden, D. A.; Philipp, D. M.; Rinaldo, D.; Halls, M. D.; Zhang, J.; Friesner, R. A., “Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences”, Int. J. Quantum Chem., 2013, 113(18), 2110-2142

Schrödinger Release 2026-1: Jaguar, Schrödinger, LLC, New York, NY, 2025.

 


 

Jaguar pKa

  • Bochevarov, A. D.; Watson, M. A.; Greenwood, J. R.; Philipp, D. M., “Multiconformation, density functional theory-based pKa prediction in application to large, flexible organic molecules with diverse functional groups”, J. Chem. Theory Comput., 2016, 12(12), 6001–6019
  • Yu, H. S.; Watson, M. A.; Bochevarov, A. D., “A weighted averaging scheme and a local atomic descriptor for pKa prediction based on density functional theory”, J. Chem. Inf. Mod., 2018, 58, 271–286
  • Klicić, J. J.; Friesner, R. A.; Liu, S.-Y.; Guida, W. C., “Accurate prediction of acidity constants in aqueous solution via density functional theory and self-consistent reaction field methods”, J. Phys. Chem. A, 2002, 106, 1327–1335

Schrödinger Release 2026-1: Jaguar pKa, Schrödinger, LLC, New York, NY, 2025.

 


 

KNIME Extensions

Schrödinger Release 2026-1: KNIME extensions, Schrödinger, LLC, New York, NY, 2025.

 


 

LigPrep

Schrödinger Release 2026-1: LigPrep, Schrödinger, LLC, New York, NY, 2025.
 


 

LiveDesign

Schrödinger Release 2026-1: LiveDesign, Schrödinger, LLC, New York, NY, 2025.

 


 

MacroModel

  • Mohamadi, F.; Richard, N. G.; Guida, W. C.; Liskamp, R.; Lipton, M.; Caufield, C.; Chang, G.; Hendrickson, T.; Still, W. C., “MacroModel – an integrated software system for modeling organic and bioorganic molecules using molecular mechanics”, J. Comput. Chem. 1990, 11, 440–467
  • Watts, K. S.;  Dalal, P.; Tebben, A. J.; Cheney, D. L.; Shelley, J. C. “Macrocycle conformational sampling with MacroModel”, J. Chem. Inf. Model. 2014, 54(10), 2680–2696

Schrödinger Release 2026-1: MacroModel, Schrödinger, LLC, New York, NY, 2025.
 


 

Maestro

Schrödinger Release 2026-1: Maestro, Schrödinger, LLC, New York, NY, 2025.

 


 

Materials Science Suite

Schrödinger Release 2026-1: Materials Science Suite, Schrödinger, LLC, New York, NY, 2025.
 


 

Materials Coarse-Grain

  • Coscia, B. J.; Shelley, J. C.; Browning, A. R.; Sanders, J. M.; Chaudret, R.; Rozot, R.; Léonforte, F.; Halls, M. D.; Luengo, G. S. “Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations”, Phys. Chem. Chem. Phys2023, 25, 1768-1780
  • Afzal, M. A. F.; Lehmkemper, K.; Sobich, E.; Hughes, T. F.; Giesen, D. J.; Zhang, T.; Krauter, C. M.; Winget, P.; Degenhardt, M.; Kyeremateng, S. O.; Browning, A. R.; Shelley, J. C. “Molecular-level examination of amorphous solid dispersion dissolution”, Mol. Pharmaceutics, 2021, 18, 11, 3999–4014 

Schrödinger Release 2026-1: Materials Coarse-Grain, Schrödinger, LLC, New York, NY, 2025.
 


 

Materials Penetrant Loading

  • Sanders, J. M.; Misra, M.; Mustard, T. J. L.; Giesen, D. J.; Zhang, T.; Shelley, J.; Halls, M. D.  “Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods”, Carbohydrate Polymers, 2021, 252, 117161

Schrödinger Release 2026-1: Materials Science Penetrant Loading, Schrödinger, LLC, New York, NY, 2025.
 


 

Phase

  • Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A., “PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening. 1. Methodology and preliminary results”, J. Comput. Aided Mol. Des., 2006, 20, 647-671
  • Dixon, S. L.; Smondyrev, A. M.; Rao, S. N., “PHASE: A novel approach to pharmacophore modeling and 3D database searching”, Chem. Biol. Drug Des., 2006, 67, 370-372

Schrödinger Release 2026-1: Phase, Schrödinger, LLC, New York, NY, 2025.

 


 

Phenix/OPLS

  • van Zundert, G. C. P.; Moriarty, N. W.; Sobolev, O. V.; Adams, P. D.; Borrelli, K. W., “Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality”, Structure., 2021, 29(8), 913-921

Schrödinger Release 2026-1: Phenix/OPLS, Schrödinger, LLC, New York, NY, 2025.

 


 

PIPER

  • Chuang, G-Y.; Kozakov, D.; Brenke, R.; Comeau, S. R.; Vajda, S., “DARS (Decoys As the Reference State) potentials for protein-protein docking”, Biophys. J., 2008, 95, 4217-4227
  • Kozakov, D.; Brenke, R.; Comeau, S. R.; Vajda, S., “PIPER: An FFT-based protein docking program with pairwise potentials”, Proteins, 2006, 65, 392-406
  1. https://rosettadesigngroup.com/blog/535/capri-state-of-protein-protein-docking/
  2. https://www.ebi.ac.uk/msd-srv/capri/

 Schrödinger Release 2026-1: PIPER, Schrödinger, LLC, New York, NY, 2025.

 


 

Prime

Schrödinger Release 2026-1: Prime, Schrödinger, LLC, New York, NY, 2025.

 


 

PrimeX

Schrödinger Release 2026-1: PrimeX, Schrödinger, LLC, New York, NY, 2025.

 


 

Protein Preparation Workflow

Schrödinger Release 2026-1: Protein Preparation Workflow; Epik,  Schrödinger, LLC, New York, NY, 2024; Impact, Schrödinger, LLC, New York, NY; Prime, Schrödinger, LLC, New York, NY, 2025.
 


 

PyMOL

For instructions on citing PyMOL, please visit www.pymol.org/citing.

 


 

QikProp

Schrödinger Release 2026-1: QikProp, Schrödinger, LLC, New York, NY, 2025.

 


 

QSite

  • Murphy, R. B.; Philipp, D. M.; Friesner, R. A., “A mixed quantum mechanics/molecular mechanics (QM/MM) method for large-scale modeling of chemistry in protein environments”, J. Comp. Chem., 2000, 21, 1442-1457
  • Philipp, D. M.; Friesner, R. A., “Mixed ab initio QM/MM modeling using frozen orbitals and tests with alanine dipeptide and tetrapeptide”, J. Comp. Chem., 1999, 20, 1468-1494

Schrödinger Release 2026-1: QSite, Schrödinger, LLC, New York, NY, 2025.

 


 

Semiempirical NDDO

Schrödinger Release 2026-1: Semiempirical NDDO protocol; Jaguar, Schrödinger, LLC, New York, NY, 2024; MOPAC, Schrödinger, LLC, New York, NY, 2025.

 


 

Shape Screening

  • Sastry, G. M.; Dixon, S. L.; Sherman, W., “Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring”, J. Chem. Inf. Model., 2011, 51, 2455-2466

Schrödinger Release 2026-1: Phase, Schrödinger, LLC, New York, NY, 2025.

 


 

SiteMap

Schrödinger Release 2026-1: SiteMap, Schrödinger, LLC, New York, NY, 2025.

 


 

WaterMap

Schrödinger Release 2026-1: WaterMap, Schrödinger, LLC, New York, NY, 2025.

 


 

WScore

  • Murphy, R. B.; Repasky, M. P.; Greenwood, J. R.; Tubert-Brohman, I.; Steven Jerome, S.; Annabhimoju, R.; Boyles, N. A.; Schmitz, C. D.; Abel, R.; Farid, R.; and Friesner, R. A., “WScore: A flexible and accurate treatment of explicit water molecules in ligand–receptor docking” Med. Chem. 2016, 59(9), 4364–4384

Schrödinger Release 2026-1: WScore, Schrödinger, LLC, New York, NY, 2025.

Services & Collaborations

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Services and collaborations to advance materials science research

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Each materials design and development project is unique. Work closely with our materials science team to tackle your challenging problems by deploying digital chemistry strategies to guide rapid materials design and optimization.

We offer highly customized services for your materials R&D projects.

Learn more
Research Collaborations

Strategic partnerships are longer-term engagements. As partners, we have shared goals and responsibilities. By working as one team with shared expertise, resources, and costs, we can mitigate risks, foster creative problem-solving, accelerate breakthrough discoveries, open doors to new products, and strengthen competitive advantage in the market, driving collective success.

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How Schrödinger’s materials science domain experts ensure partner success

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Access expert support, educational materials, and training resources designed for both novice and experienced users.

Pharmaceutical formulations

Pharmaceutical formulations

Pharmaceutical formulations


Molecular and periodic quantum mechanics, all- atom molecular dynamics, and coarse-grained approaches for studying active pharmaceutical ingredients and their formulations

Details
Modules
6
Duration
6 weeks / ~25 hours to complete
Level
Introductory
Cost
$600 for non-student users
$160 for student / post-doc
Course Timeframe
When registering for the course, you will be able to choose your preferred start and end date. Within those dates, you will have asynchronous access to the course to work on your preferred schedule

Overview

Computational molecular modeling tools have proven effective in materials science research and development. Chemists, physicists and engineers working in materials science will increasingly encounter molecular modeling throughout their careers, making it critical to have a foundational understanding of the cutting edge tools and methods. These courses are ideal for those who wish to develop professionally and expand their CV by earning certification and a badge.

These computational chemistry courses offer an effective and efficient approach to learn practical computational chemistry for materials science:

  • Work hands-on with Schrödinger’s industry-leading Materials Science Maestro software
  • Jump start your research program by learning methods that can be directly applied to ongoing projects
  • Learn topics ranging from density functional theory (DFT) to molecular dynamics to machine learning for materials design
  • Perform a completely independent case study to demonstrate mastery of the course content
  • Benefit from review and feedback from Schrödinger Education Team experts for course assignments and course-related queries
  • Work on the course materials on your own schedule whenever convenient for you

 

This course comes with access to a web-based version of Schrödinger software with the necessary licenses and compute resources for the course:

Requirements
  • A computer with reliable high speed internet access (8 Mbps or better)
  • A mouse and/or external monitor (recommended but not required)
  • Working knowledge of general chemistry
Certification
  • A certificate signed by the Schrödinger course lead
  • A badge that can be posted to social media, such as LinkedIn
background pattern

What you will learn

MS Maestro interface

Learn how to use an industry-leading interface for materials science modeling. No coding or scripting required to run modeling workflows

Molecular and periodic quantum mechanics

Learn to apply molecular and periodic density functional theory (DFT) for automated property prediction for amorphous and crystalline active pharmaceutical ingredients

Molecular dynamics

Learn to leverage all-atom MD simulations for simulating properties of complete formulations including miscibility and hygroscopicity

Coarse-grained modeling

Access larger length scale and longer time scales by employing coarse-grained methods to study formulations

Modules

Module 1
2 Hours

Introduction to materials modeling

Video
Video

Introduction to Materials modeling & this online course

Video Tutorial
Video tutorial

Introduction to materials science (MS) Maestro

Video
Video

Modeling for pharmaceutical formulations

End checkpoint
Honor code agreement and checkpoint
Module 2
6 Hours + Compute Time

Molecular & periodic quantum mechanics

Video
Video

Introduction to quantum mechanics (mQM & pQM)

Tutorial
Tutorials
  • Quantum mechanical workflows and properties
  • API degradation
  • pKa predictions
  • Spectroscopy (molecular)
  • Building and Manipulating Crystals
  • Properties of Bulk Molecular Crystals
  • Spectroscopy (solid-state)
End checkpoint
End of module checkpoint
Module 3
5 Hours + Compute Time

All-atom molecular dynamics

Video
Video

Introduction to molecular dynamics (MD)

Tutorial
Tutorials
  • Disordered system building and MD multistage workflows
  • Molecular dynamics simulations for API (active pharmaceutical ingredient) miscibility
  • Glass transition temperature for APIs
  • Hygroscopicity
  • Crystal morphology
End checkpoint
End of module checkpoint
Module 4
5 Hours + Compute Time

Coarse-grained simulation

Video
Video

Introduction to coarse-graining (CG)

Tutorial
Tutorials
  • Ibuprofen cyclodextrin inclusion complexes with the martini coarse-grained force field
  • Ibuprofen copovidone drug excipient model with dissipative particle dynamics (DPD)
End checkpoint
End of module checkpoint
Module 5
2 Hours + Compute Time

Machine learning

Video
Video

Introduction to machine learning (ML)

Tutorial
Tutorials
  • Machine learning for materials science
  • Machine learning for formulations
End checkpoint
End of module checkpoint
Module 6
2 Hours + Compute Time

Guided case study

Tutorial
Case studies
  • Nanoemulsions with automated DPD parameterization
  • Building pH dependent systems of Diclofenac
End checkpoint
End of module checkpoint
Module 7
4 hours + Compute Time

Independent case study

Assignment
Assignment

API property prediction

Course completion
Course completion and certification
Self-paced video lessons on materials modeling

Self-paced video lessons on materials modeling

Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)

Access cloud-based computing resources to perform calculations yourself

Access cloud-based computing resources to perform calculations yourself

Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)

Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)

Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)

Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)

On-demand video lessons on materials modeling

On-demand video lessons on materials modeling

Access cloud-based computing resources to perform calculations yourself

Access cloud-based computing resources to perform calculations yourself

Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)

Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)

Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)

Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)

Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

Self-paced video lessons on materials modeling
Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)
Access cloud-based computing resources to perform calculations yourself
Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)
Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)
Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)
On-demand video lessons on materials modeling
Access cloud-based computing resources to perform calculations yourself
Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)
Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)
Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)
Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

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We proudly support the next generation of scientists and are committed to providing opportunities to those with limited resources. Learn about your funding options for our online certification courses as a student, post-doc, or industry scientist and enroll today!

What our alumni say

“Clear instructions with a well-designed interface allowed me to run some of my own first molecular dynamics simulations. The information from the course felt much more secure than the information from YouTube because I knew it was developed by experts”
Graduate Student
“The course let me talk confidentially about molecular modeling and what it can do. For me, this was a nice experience which left me with many ideas for applying molecular modeling in the research area of our department, not only for me but also for my colleagues.”
Graduate Student
“As always, the course is very well designed. Formulation is quite outside my comfort zone in terms of theory and modeling but this course provided me with knowledge of evaluating what modeling can facilitate in the real world. Really great design and education process.”
Senior DirectorTherapeutic Protein Design

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When you complete a course with us in molecular modeling and are ready to share what you learned with your colleagues and employers, you can share your certificate and badge on your LinkedIn profile.

Frequently asked questions

How much do the online courses cost?

Pricing varies by each course and by the participant type. For students wishing to take these courses, we offer a student price of $150 for introductory courses, $305 for the Materials Science bundle, and $870 for advanced courses. For commercial participants, the course price is $575 for introductory courses and $1435 for advanced courses and bundles.

When does the course start?

The courses run on sessions, which range from 3-6 week periods during which the course and access to software are available to participants. You can find the course session and start dates on each course page.

What time are the lectures?

Once the course session begins, all lectures are asynchronous and you can view the self-paced videos, tutorials, and assignments at your convenience.

How could I pay for this course?

Interested participants can pay for the course by completing their registration and using the credit card portal for an instant sign up. Please note that a credit card is required as we do not accept debit cards. Additionally, we can provide a purchase order upon request, please email online-learning@schrodinger.com if you are interested in this option. If you have any questions regarding how to pay for the course, please visit our funding options page.

How can I preview the course before registering?
Are there any scholarship opportunities available for students?

Schrödinger is committed to supporting students with limited resources. Schrödinger’s mission is to improve human health and quality of life by transforming the way therapeutics and materials are discovered. Schrödinger proudly supports the next generation of scientists. We have created a scholarship program that is open to full-time students or post-docs to students who can demonstrate financial need, and have a statement of support from the academic advisor. Please complete the application form if you qualify for our scholarship program!

Will material still be available after a course ends?

While access to the software will end when the course closes, some of the material within the course (slides, papers, and tutorials) are available for download so that you can refer back to it after the course. Other materials, such as videos, quizzes, and access to the software, will only be available for the duration of the course.

Do I need access to the software to be able to do the course? Do I have to purchase the software separately?

For the duration of the course, you will have access to a web-based version of Maestro, Bioluminate, Materials Science Maestro and/or LiveDesign (depending on the course). You do not have to separately purchase access to any software. While access to the software will end when the course closes, some of the material within the course (slides, papers, and tutorials) are available for download so that you can refer back to it after the course. Other materials, such as videos, quizzes, and access to the software, will only be available for the duration of the course. Please note that Schrödinger software is only to be used for course-related purposes.

Related Courses

Online certification course: Level-up your skill set in catalysis modeling Materials Science Materials Science
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Molecular quantum mechanics and machine learning approaches for studying reactivity and mechanism at the molecular level

Molecular modeling for materials science applications: course bundle Materials Science Materials Science
Course bundle

Access all materials science courses with a single, discounted registration

Molecular modeling for materials science applications: Polymeric materials course Materials Science Materials Science
Polymeric materials

All-atom molecular dynamics and machine learning approaches for studying polymeric materials and their properties under various conditions

Supporting Associations

nanoHUB

Japan

計算科学の、その先へ。

計算科学の、その先へ。

極限の精度と、先端のスピード。
分子シミュレーションの新たな世界を現実へ。

News

Event | Materials Science
JUN 30, 2026 | Frontiers in Digital Chemistry: Tokyo | Day 2 Pharmaceutical Formulation Workshop

「次世代デジタル製剤開発 実践ワークショップ」を開催します。
聴講型のセミナーだけではなく実践的なワークショップをご用意しております。

Blog
化学製造プロセスの高効率化に向けた触媒ポートフォリオの構築

化学業界は転換期を迎えており、コペルニック・カタリスト社は、コスト削減、排出量削減、そして飛躍的な効率化を実現する次世代触媒という、業界をリードする技術の開発に取り組んでいます

Webinars

Webinar | Materials Science
原子層堆積(ALD)における理論と実験の融合 ― シリコンカーボナイトライド成膜プロセス設計 | Atomic layer deposition: Bridging theory and experiment to design a process for silicon carbonitride

MAR 19, 2026 | SchrödingerとLam Researchのコラボレーション事例を通じて、計算科学(DFT)と実験(RGA、FTIR)を組み合わせ、最適な前駆体を効率的に選定するアプローチをご紹介します。

Webinar | Materials Science
難溶性薬物の放出メカニズムを解明する – ASD研究の新たなアプローチ | Modelling amorphous solid dispersion (ASD) release mechanisms

OCT 15, 2025 | AbbVie と Schrödinger のエキスパートが、ASDにおける薬物放出やLoss of Release のメカニズムを、熱力学モデリング・分子シミュレーション・実験研究 を組み合わせた最新の研究成果を基に解説します。

Webinar | Materials Science
Advancing machine learning force fields for materials science applications | 最新機能 MPNICEのご紹介

SEPT 18, 2025 |シュレーディンガーが開発した最先端のMLFFアーキテクチャ「MPNICE」をご紹介します。

Expand the impact of structural biology on drug design Webinar | Life Science
JUL 16, 2025 | Schrödinger デジタル創薬セミナー18 | Enabling cryoEM structures for drug discovery with the Schrödinger Suite

創薬において、タンパク質構造の有用性と価値は、分子の物性を合理的に最適化する能力に直接関係しています。

Webinar | Life Science
Webinar | Life Science
Webinar | Materials Science
FEB 19, 2025 | Virtual testing of personal care and cosmetics formulations using digital chemistry methods

持続可能な製品の開発には多くの課題があり、時間やリソース、新しい原材料が必要です。

Webinar | Life Science

事業内容
Corporate profile

材料研究開発を加速し、マテリアルズ・インフォマティクスにも対応! 計算化学ソリューションのパイオニア

シュレーディンガー株式会社は、アメリカ・ニューヨークに本社を置く、Schrödinger Inc. の日本法人です。Schrödingerは、ライフ・サイエンスとマテリアル・サイエンス分野を中心に、化学とコンピュータ・サイエンスの先進技術を融合させたソフトウエアの開発に約30年の歴史を持ち、創薬、バイオロジクス、材料分野の研究開発に高度なソリューションを提供します。

事業内容

【ソフトウェア開発・販売】

    • 創薬およびバイオテクノロジー研究を加速する、化学シミュレーション・ソフトウェア
    • 高速分子シミュレーションにより、ポリマー、有機EL、半導体をはじめとして、さまざまな材料開発を総合支援する Materials Science Suite
    • 計算化学の導入障壁を取り除く、データ蓄積・活用ソ リューション LiveDesign

    【ソリューション提案・コラボレーション・共同研究】

    お客様のご状況に応じて、各分野の専門サイエンティストが適切なソリューションをご提案いたします

会社概要
Contact information

社名
シュレーディンガー株式会社
Schrödinger K.K.

住所
〒100-0005
東京都千代田区丸の内1-8-1
丸の内トラストタワーN館 13階

Tel
+81-3-4520-7090

Fax
+81-3-4520-7091

主な事業内容
日本におけるSchrödinger製品の営業
・サポート統括会社

代表者
代表取締役 ケネス・パトリック・ロートン

親会社
Schrödinger, Inc.
1990年に設立され、現在では米国各所、
ヨーロッパ、インド、日本、中国、
韓国を拠点として事業を展開しています。

※営業を目的としたお問合わせはご遠慮ください。

Hit-to-Lead & Lead Optimization

Hit-to-Lead & Lead Optimization

Design better quality drug candidates, faster

Hit-to-Lead & Lead Optimization

Explore and triage vast chemical space with high precision in silico tools

Identifying the best drug candidate — a novel molecule that optimizes key physicochemical properties while maintaining on-target potency and specificity — is the ultimate challenge of lead optimization programs.

Schrödinger’s platform for molecular design empowers project teams to deploy a ‘predict-first’ approach to lead optimization challenges, dramatically expanding the pool of molecules that can be explored through highly interactive, fully in silico design cycles. Teams can confidently spend time and energy exploring new, unknown, and often more complex designs while sending only the top performing molecules for synthesis.

Diverse solutions for chemical enumeration, property prediction, and team collaboration

Create and explore project-relevant chemical space to fast-track ligand design

Create and tailor your own chemical space using reaction or R-group based enumeration and advanced filtering capabilities
Combine accurate physics-based simulations with the power of machine learning to efficiently explore vast chemical space
Profile billions of virtual target-specific molecules with an intelligent, reaction-based enumeration, filtering and accurate FEP+ scoring workflow

Drive ligand design by leveraging the thermodynamics of water interactions in active sites 

Discover new potency drivers by predicting the location and thermodynamic potential of hydration sites in the binding site
Visualize hydration sites for an easy and intuitive method of interpreting SAR

Design and collaborate in real-time with your colleagues — anytime, anywhere

Share, revise, and test design ideas with team members using a single cloud-native platform, LiveDesign
Capture decisions and hypotheses to improve collective SAR understanding and accelerate compound progression
Build rich dashboards to analyze whole project data or individual molecules and quickly identify promising design opportunities in key property space

Predict key properties to accelerate ligand optimization

Free energy-based computational assay (FEP+):

• Potency
• Selectivity
• Solubility

Other physics-based predictions:

• Membrane permeability
• hERG inhibition
• CYP inhibition / TDI
• CYP induction (DDI)
• Site of metabolism
• Brain exposure

Case studies

Discover how Schrödinger technology is being used to solve real-world research challenges.

Life Science Case Study

Design of a highly selective, allosteric, picomolar TYK2 inhibitor using novel FEP+ strategies

Life Science Case Study

Design of a novel, potent CDC7 inhibitor development candidate with high ligand efficiency and optimized properties

Life Science Case Study

High precision, computationally-guided discovery of highly selective Wee1 inhibitors for the treatment of solid tumors

Life Science Case Study

Discovery of a novel, potent ACC inhibitor driven by computationally-guided design and assessment of water energetics in the binding site

Life Science Case Study

Hit to development candidate in 10 months: Rapid discovery of a novel, potent MALT1 inhibitor

Life Science Case Study

Indiana Biosciences Research Institute enables drug discovery using CDD Vault and Schrödinger LiveDesign

Life Science Case Study

Schrödinger solutions for small molecule protonation state enumeration and pKa prediction

Life Science Case Study

Morphic Therapeutic leverages digital chemistry strategy to design a novel small molecule inhibitor of α4β7 integrin

Life Science Case Study

Hit to lead design of novel d-amino-acid oxidase inhibitors using a comprehensive digital chemistry strategy

Life Science Case Study

Stories from drug discovery: Modeling strategies in the pursuit of development candidate in oncology program 1

  • Life Science
  • Webinar

Design of a highly selective, allosteric, picomolar TYK2 inhibitor in clinical development

In this webinar, we highlight key moments from the discovery of this potentially best-in-class selective, allosteric, picomolar inhibitor of TYK2.

Watch webinar
  • Life Science
  • Webinar

Impacting drug discovery programs with large-scale de novo design

In this webinar, scientists from Schrödinger’s therapeutics group describe several recent case studies where de novo design technologies have allowed teams to overcome critical design challenges and accelerate programs.

Watch webinar

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Life Science Documentation

Learning Path: Oligonucleotide Modeling

A structured overview of tools and workflows for nucleic acids in drug discovery.

Life Science Tutorial

Predicting Drug Residence Times from Unbinding Kinetics Simulations

Run the unbinding kinetics workflow on a kinase system and analyze the results.

Life Science Tutorial

Forming RNA – Ligand Interactions with Ligand Designer

Modify ligand bound to RNA receptor to improve binding affinity using Ligand Designer.

Life Science Documentation

Membrane Permeability

Calculate the passive membrane permeability of a set of congeneric ligands.

Life Science Documentation

FEP+

Computational prediction of protein-ligand binding using physics-based free energy perturbation technology at an accuracy matching experimental methods.

Life Science Documentation

Ligand Designer

Interactively design a ligand in the context of a protein or DNA/RNA receptor to optimize its binding and properties.

Life Science Documentation

IFD-MD

GPU-accelerated prediction of receptor-ligand binding poses.

Life Science Documentation

Glide

Easy-to-use, reliable ligand-receptor docking.

Life Science Documentation

DeepAutoQSAR

Predict molecular properties based on chemical structure using machine learning (ML).

Life Science Documentation

ConfGen

ConfGen documentation including online help and user manual.

Key Products

Learn more about the key computational technologies available to progress your research projects.

Maestro

Complete modeling environment for your molecular discovery

FEP+

High-performance free energy calculations for drug discovery

Active Learning Applications

Accelerate discovery with machine learning

De Novo Design Workflow

Fully-integrated, cloud-based design system for ultra-large scale chemical space exploration and refinement

IFD-MD

Accurate ligand binding mode prediction for novel chemical matter, for on-targets and off-targets

WaterMap

State-of-the-art, structure-based method for assessing the energetics of water solvating ligand binding sites for ligand optimization

LiveDesign

Your complete digital molecular design lab

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Life Science Publication

Accelerated in silico discovery of SGR-1505: A potent MALT1 allosteric inhibitor for the treatment of mature B-cell malignancies

Life Science Publication

Discovery of highly potent noncovalent inhibitors of SARS-CoV-2 main protease through computer-aided drug design

Life Science Publication

Harnessing free energy calculations to achieve kinome-wide selectivity in drug discovery campaigns: Wee1 case study

Life Science Publication

Computational Hit Finding: An Industry Perspective

Life Science Publication

Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity

Life Science Publication

Structure-based discovery and development of highly potent dihydroorotate dehydrogenase inhibitors for malaria chemoprevention

Life Science Publication

Leveraging the thermodynamics of protein conformations in drug discovery

Life Science Publication

In silico enabled discovery of KAI-11101, a preclinical DLK inhibitor for the treatment of neurodegenerative disease and neuronal injury

Life Science Publication

Discovery of a novel mutant-selective epidermal growth factor receptor inhibitor using an in silico enabled drug discovery platform

Life Science Publication

The Discovery of MORF-627, a Highly Selective Conformationally-Biased Zwitterionic Integrin αvβ6 Inhibitor for Fibrosis

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Thin film processing

Thin Film Processing

Fast-track the next generation of electronic devices

Thin film processing

Optimize semiconductor processing with the power of digital simulations

Scientists in the semiconductor industry are under constant pressure to deliver electronic devices that are smaller, more powerful, and more energy-efficient. Fabricating the next generation of device structures at the micro- or nano-scale is a huge and growing challenge.

Schrödinger’s Materials Science platform offers advanced computational tools to help companies optimize atomic-level processing for electronics and other high-tech industries, and improve device performance.

background pattern

Intuitive computational workflows designed by experts in thin film processing

Easy-to-use system builders for all material types
Powerful workflows for molecular simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

Solutions for the material and process challenges facing today’s electronics industry

Optimize precursor materials

  • Compute key physical and chemical properties including volatility and thermal stability of precursors for thin film deposition or etch
  • Gain insights in silico into the chemistry at the surface, where precursors react with substrates
  • Speed up new precursor discovery with large-scale screening and machine learning

Refine semiconductor fabrication

  • Identify the best precursor and co-reagent combinations for wafer processing by MOCVD, ALD, and etch
  • Predict surface reactivity to find the optimum windows for experimental process parameters
  • Gain atomic-level insights to help troubleshoot processes

Simulate materials for more predictable chip and device manufacturing

  • Simulate materials properties that complement metrology and provide insights into device performance
  • Identify root causes of device failure by investigating materials at the atomic level
  • Test and screen new material combinations in silico

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Atomic layer deposition: Bridging theory and experiment to design a process for silicon carbonitride

MAR 19, 2026 | SchrödingerとLam Researchのコラボレーション事例を通じて、計算科学(DFT)と実験(RGA、FTIR)を組み合わせ、最適な前駆体を効率的に選定するアプローチをご紹介します。

Materials Science Webinar

Digital forum on atomic layer deposition: Bridging theory and experiment to design a process for silicon carbonitride

Join us as we discuss how effectively theory and experiment are working together to solve the R&D challenges facing high-tech industries.

Materials Science Webinar

Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Materials Science Webinar

Advancing machine learning force fields for materials science applications

In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Purposeful simulation: Maximising impact in surface chemistry modelling

In this webinar, learn about a variety of atomistic models of surfaces and gain perspective on the underlying rationale, benefits and limitations of each.

Materials Science Webinar

Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

Materials Science Webinar

Efficient Computation of Process Parameters for Controlling the Chemistry of Deposition or Etch

In this webinar, we illustrate how atomic-scale DFT can be embedded into higher-level computational schemes for accurate and achievable prediction of the conditions and parameters for controlling chemical processes.

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Online certification courses

Molecular modeling for materials science applications Materials Science Materials Science
Surface chemistry

Molecular quantum mechanics, periodic quantum mechanics, and machine learning approaches for studying atomic layer processing and heterogeneous catalysis

Online certification course: Level-up your skill set in catalysis modeling Materials Science Materials Science
Homogeneous catalysis & reactivity

Molecular quantum mechanics and machine learning approaches for studying reactivity and mechanism at the molecular level

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Catalytic Selectivity Through Microkinetic Modeling

Learn to analyze the selectivity of the catalytic oxidation of CO and H2 on a Pd(111) surface using Microkinetic Modeling (MKM) calculations.

Materials Science Tutorial

Locating Adsorption Sites on Surfaces

Learn how to locate adsorption sites on surfaces.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Documentation

MS Surface

A solution for heterogeneous catalysis and materials processing.

Materials Science Documentation

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry.

Materials Science Documentation

MS Informatics

Automated machine learning tools for materials science applications

Materials Science Documentation

MS Microkinetics

An efficient tool for surface reaction kinetics.

Materials Science Documentation

Quantum ESPRESSO Interface

A comprehensive graphical user interface for calculation set-up, job control and results analysis.

Materials Science Tutorial

Applied Machine Learning for Formulations

Learn to apply the Formulation Machine Learning Panel across a range of materials applications. This tutorial assumes that you have already completed the Machine Learning for Formulations tutorial.

Materials Science Tutorial

Machine Learning for OLED Device Design

Learn to train a machine learning model to predict properties of OLED devices and subsequently apply this trained model to predict target properties for new OLED devices unseen during training.

Key Products

Learn more about the key computational technologies available to progress your research projects.

MS Surface

Solution for heterogeneous catalysis and materials processing

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

MS Informatics

Automated machine learning tools for materials science applications

MS Maestro

Complete modeling environment for your materials discovery

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

LiveDesign

Your complete digital molecular design lab

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

Atomic Layer Deposition of NiOx: Harnessing the Potential of New Precursor Combinations for Photoelectrochemical Water Oxidation

Materials Science Publication

Finding the temperature window for atomic layer deposition of ruthenium metal via efficient phonon calculations

Materials Science Publication

Investigation of the atomic layer etching mechanism for Al2O3 using hexafluoroacetylacetone and H2 plasma

Materials Science Publication

Chemical Nature and Control of High-k Dielectric/III-V Interfaces

Materials Science Publication

Study of Electronic Structure and Simulation of Molecular Rearrangements of MOCVD Precursors to Predict Their Thermal Stability Upon Evaporation on the Example of Heteroleptic Copper(II) Complexes

Materials Science Publication

Schiff base as n-type semiconductor: synthesis, characterization, and diode features

Materials Science Publication

Boronic Acid-Based n-Type Semiconductor for Electronic Device Application

Materials Science Publication

Enhancement of NO2 gas sensing ability through strong binding energy by modification of interface characteristics

Materials Science Publication

Thermal Grafting of Benzaldehyde for Preparing Catalytically Active Silicon Surface Evaluated by Electrical Methods

Materials Science Publication

Prediction and Validation of the Process Window for Atomic Layer Etching: HF Exposure on TiO2

Software & services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Hit Discovery

HIT DISCOVERY

Modernize your hit discovery workflows

Modernize Hit Discovery Workflows

Discover higher-quality, more diverse hits

As drug discovery teams pursue more challenging targets against tight timelines, there is a need for more accurate and efficient hit finding solutions. The emergence of ultra-large, on-demand commercial chemical libraries is further driving the need for technologies that can efficiently screen and score broad chemical space.

Schrödinger, a long-time industry leader in computational hit discovery, is spearheading modern virtual screening technologies and workflows that enable efficient, large-scale chemical exploration.

CAPABILITIES

Full breadth of structure-based and ligand-based screening solutions

Screen billions of compounds with structure-based methods

Accurately screen virtual chemical libraries using an industry-leading docking technology
Accelerate docking across millions to billions of compounds by leveraging the power of active learning
Easily bias docking calculations to match your desired chemical space using a broad range of available constraints
Move beyond the traditional limits of fragment libraries with ultra-large in silico fragment-based drug discovery

Boost your hit success rates with highly accurate rescoring technologies

Identify the best hits from your virtual screens using the most accurate method available — absolute binding free energy calculations
Accelerate absolute binding free energy calculations across large compound libraries using active learning
Analyze top-scoring hits interactively by combining property-based and structure-based selections

Leverage known chemical matter to quickly identify novel starting points

Enumerate synthetically-tractable core designs starting from existing chemical matter
Prioritize novel core designs using a benchmark free energy perturbation method

Efficiently execute ligand-based screens using shape or pharmacophore features

Screen ultra-large libraries quickly with the 3D shape overlap between known active ligand conformations and library molecules
Easily create and validate pharmacophore hypotheses and use them to screen compound libraries, optionally taking into account receptor information

Jumpstart your virtual screens with prepared commercial libraries

Purchase prepared libraries ranging from a few million compounds to tens of billions of compounds from Enamine, Sigma Aldrich, MolPort, WuXi, and Mcule.

Enamine Logo
Mcule
MolPort
Wuxi Apptec

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Life Science Webinar

Water matters: Enhancing early drug discovery with insights from water energetics

In this webinar, we discuss the impact of two technologies that leverage explicit water energetics in the binding pocket to enhance drug design—WaterMap and Glide WS.

Life Science Webinar

アーカイブ配信: QuickShape Screening in the Age of Ultra-large Libraries

手軽に入手できる化合物の数が急速に増加する一方、創薬ターゲットに実際に作用する新規化合物探索のニーズは依然として高いままです。

Life Science Webinar

Empowering scientists with integrated AI/ML modeling for rapid molecular property predictions

In this webinar, we present LiveDesign ML, a new module in Schrödinger’s LiveDesign collaborative enterprise informatics platform, for training and deploying state-of-the-art AI/ML models with minimal manual intervention.

Life Science Case Study

Design of a highly selective, allosteric, picomolar TYK2 inhibitor using novel FEP+ strategies

Life Science Webinar

Modern Virtual Screening Technologies 利用薛定谔数字化平台进行现代虚拟筛选

Life Science Webinar

The Predict-First Paradigm: How Digital Chemistry is Shaping the Future of Drug Discovery 预测优先范式: 数字化学如何塑造药物发现的未来

Life Science Webinar

 Schrödinger デジタル創薬セミナー: Into the Clinic ~計算化学がもたらす創薬プロセスの変貌~  

AI/Machine LearningによるアクティブラーニングとAbsolute Binding FEP+を活用した新しいバーチャルスクリーニング手法最新の創薬研究事例を紹介します。

Life Science Webinar

Schrödinger デジタル創薬セミナー: Impacting drug discovery programs with large-scale de novo design

高品質な化学物質の創製をより包括的かつ効果的に可能にする技術の開発は、薬物探索の長年の目標でした。

Life Science Webinar

Impacting Drug Discovery Programs with Large-Scale De Novo Design

Developing technologies to more comprehensively and effectively enable de novo design of high-quality chemical matter has been a long-standing goal of drug discovery.

Life Science Webinar

Chinese Webinar: 薛定谔中文讲座:DLK在计算机辅助药物设计中的案例研究 ,网络讲座录制 计算机驱动用于治疗神经退行性疾病的高效、高选择性和穿透脑血屏障的DLK抑制剂的发现

双亮氨酸拉链激酶(DLK)(又名MAP3K12)是混合系谱激酶(MLK)家族的成员,它包含一个N-末端激酶结构域,后面跟着两个亮氨酸拉链结构域以及一个富含甘氨酸/丝氨酸/脯氨酸的C-末端结构域。它主要在神经元细胞中表达,特别是在神经元的突触末端和轴突中

Featured CourseHigh-Throughput Virtual Screening for Hit Finding and Evaluation

Learn virtual screening with our hands-on, online certification course

Level-up your virtual screening skills and enroll in our online molecular modeling course, High-Throughput Virtual Screening for Hit Finding and Evaluation.

Learn More

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Life Science Documentation

Learning Path: Oligonucleotide Modeling

A structured overview of tools and workflows for nucleic acids in drug discovery.

Life Science Tutorial

Small Molecule – Oligonucleotide Docking with Glide

Generate receptor grid, dock co-crystal and congeneric ligands, and analyze the results.

Life Science Documentation

Shape Screening

A ligand-based workflow for efficiently screening ultra-large purchasable or synthesizable compound libraries.

Life Science Documentation

Prime

A fully-integrated protein structure prediction solution that incorporates homology modeling and fold recognition into a single solution.

Life Science Documentation

Phase

An intuitive pharmacophore modeling tool that allows assessment of compounds based on the steric and electronic features of molecules known to have biological activity.

Life Science Documentation

FEP+

Computational prediction of protein-ligand binding using physics-based free energy perturbation technology at an accuracy matching experimental methods.

Life Science Documentation

Glide

Easy-to-use, reliable ligand-receptor docking.

Life Science Documentation

DeepAutoQSAR

Predict molecular properties based on chemical structure using machine learning (ML).

Life Science Documentation

ConfGen

ConfGen documentation including online help and user manual.

Life Science Documentation

Active Learning Applications

Active Learning Glide documentation including online help and user manual.

Key Products

Learn more about the key computational technologies available to progress your research projects.

Glide

Industry-leading ligand-receptor docking solution

Active Learning Applications

Accelerate discovery with machine learning

FEP+

High-performance free energy calculations for drug discovery

Phase

An easy-to-use pharmacophore modeling solution for ligand- and structure-based drug design

Shape Screening

Efficient ligand-based virtual screening of millions to billions of molecules

Prepared Commercial Libraries

Fully prepared databases of purchasable compounds

Maestro

Complete modeling environment for your molecular discovery

LiveDesign

Your complete digital molecular design lab

Hit Discovery Services 

Find more diverse hits, faster

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Life Science Publication

Accelerated in silico discovery of SGR-1505: A potent MALT1 allosteric inhibitor for the treatment of mature B-cell malignancies

Life Science Publication

Discovery of highly potent noncovalent inhibitors of SARS-CoV-2 main protease through computer-aided drug design

Life Science Publication

Knowledge and structure-based drug design of 15-PGDH inhibitors

Life Science Publication

Harnessing free energy calculations to achieve kinome-wide selectivity in drug discovery campaigns: Wee1 case study

Life Science Publication

Optimizing drug design by merging generative AI with a physics-based active learning framework

Life Science Publication

STX-721, a Covalent EGFR/HER2 Exon 20 Inhibitor, Utilizes Exon 20–Mutant Dynamic Protein States and Achieves Unique Mutant Selectivity Across Human Cancer Models

Life Science Publication

Enabling in-silico Hit Discovery Workflows Targeting RNA with Small Molecules

Life Science Publication

Computational Hit Finding: An Industry Perspective

Life Science Publication

Structure-based discovery and development of highly potent dihydroorotate dehydrogenase inhibitors for malaria chemoprevention

Life Science Publication

Leveraging the thermodynamics of protein conformations in drug discovery

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Oil & Gas

Oil & Gas

Oil & Gas

Harness molecular simulation to design materials for sustainable energy

Oil and gas companies are under increased pressure to adopt more sustainable, eco-friendly solutions while satisfying growing demand for energy and power resources.

Schrödinger’s digital chemistry platform can help meet these demands while helping limit environmental impact. Our industry-leading platform leverages advanced molecular simulation and machine learning for in silico design of novel materials for oil and gas from enhanced oil recovery to processing to waste management.

High-performance simulation solutions that meet the needs of today’s oil and gas industry

Optimize chemical production from oil and gas

Improve activity and selectivity in the heterogeneous and homogeneous catalytic synthesis of value-added chemicals.

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Improve product quality

Solve processing challenges such as desulfurization, gas purification, and cracking of heavy hydrocarbons.

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Drive more efficient oil and gas processes

Control of solid hydrate formation via virtual surfactant optimization.

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Address environmental concerns

Reduce carbon footprint and energy consumption by cutting down or re-utilizing waste and thereby increasing production efficiency.

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Get a head start in next generation energy challenges

Digitally investigate novel materials for hydrogen storage, methanol combustion, and carbon capture and utilization (CCU).

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Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Advancing machine learning force fields for materials science applications

In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Materials Science Webinar

Computational Catalysis at Schrödinger

In this webinar, we highlight the digital simulation tools specifically for Catalysis & Reactivity.

Materials Science Webinar

Taking experimentation digital: Materials innovation using atomistic simulation and machine learning at-scale

In this webinar, we introduce a modern approach to materials R&D using a digital chemistry platform for in silico analysis, optimization and discovery.

Materials Science Webinar

Automated digital prediction of chemical degradation products

In this webinar, we present Schrödinger’s enhanced Nanoreactor, expanding upon the tool developed by Grimme and co-workers with many new features, including improved energy refinement of results and integrated user interface.

Materials Science Webinar

Data-driven materials innovation: Where machine learning meets physics

In this webinar, we demonstrate how Schrödinger’s tools can help overcome these common challenges by using a combination of physics-based simulation data, enterprise informatics, and chemistry-informed ML.

Materials Science White Paper

An automated workflow for rapid large-scale computational screening to meet the demands of modern catalyst development

Featured courseMolecular modeling for materials science applications: course bundle

Molecular modeling for materials science applications: Course bundle

Online certification course: Level-up your skill set in materials innovation

Not familiar with Schrödinger software and interface? Benefit from vast educational resources, self-paced courses, and 1-1 training tailored for you. Schrödinger software is designed for experts and novices with easy-to-use interface and automated workflows, backed by dedicated scientific and technical support.

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Schedule a consultation on Schrödinger’s oil & gas solutions.

Contact us today to explore how you can leverage advanced simulation and AI/ML for oil & gas.

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Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.