Release 2024-4

Library Background

Release Notes

Release 2024-4

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Improved usability in scatter plots and histograms:
    • See relationships in data across multiple plots and histograms with streamlined menu into “Entry Actions” and “Sync Options” menu icon
    • Added support for string and boolean histograms
  • In the histogram panel, easily switch between settings and data table views
  • Specify the number of columns and rows for fine control of Workspace Tiles
  • Save animated GIF of vibrational motion from Jaguar frequency calculation
  • Enhanced Cryo-EM surface performance:
    • Up to 2x faster loading of Cryo-EM surface files
    • Up to 5x increase in speed for isosurface contour creation and adjustments
  • Refined toolbar design for enhanced simplicity, modern aesthetics, and optimization for dark mode
  • Updated Maestro Project format to version 5:
    • Support for multi-letter chain names beyond traditional 26 characters
    • Compressed .prjzip files designed for easy sharing via email
    • Automatic conversion of version 4 projects to version 5 upon opening
    • By default save Maestro Projects in version 5 format with an option to save in version 4 for backward compatibility
  • Support added for two new CIF file formats: “PDBx/mmCIF (*.cif)” and “Small Molecule CIF (.cif)”
  • Opening Maestro locally from LiveDesign is now supported on macOS, Linux, and Windows
  • Revamped splash screens & iconography: Modern visuals for an updated look and feel

Workflows & Pipelining [KNIME Extensions]

  • LiveDesign Admin node can take user credentials from the LiveDesign Connection node enabling SSO configuration

Target Validation & Structure Enablement

Protein Preparation

  • Protein The Protein Preparation Workflow now considers Epik states of ligands during the hydrogen-bond network optimization stage by default

Protein X-Ray Refinement

  • New sf2map.py script quickly generates an aligned x-ray map, given an input structure and a cif file containing structure factors

IFD-MD

  • Updated IFD-MD for automatic sampling of histidine tautomer states (HID, HIE): Consider induced fit effects simultaneously to resolve receptor tautomeric states and predict receptor and ligand conformations

Binding Site & Structure Analysis

SiteMap

  • Automatically apply Combined Mode which breaks down sites larger than 800 Å3

Mixed Solvent MD (MxMD)

  • Improved cryptic pocket identification with new mixed solvent molecular dynamics (MxMD) interface including customizable visualization (Beta): Gain a clearer understanding of candidate binding pockets on the protein surface with a new interface to set up and analyze MxMD simulations

Hit Discovery

Active Learning Applications

  • Faster time-to-results in AL-Glide and Glide using ZeroMQ mode for machine learning evaluation stage and Glide docking stage

Shape Screening

  • Additional similarity normalization schemes now available for Quick Shape and 1D Screening command. In addition to the max{O(A,A), O(B,B)} default can apply min{O(A,A), O(B,B)}, O(A,A), and O(B,B) where O(A,B) is the overlap between ligands A and B
  • Run Quick Shape and 1D Screens against a Phase pharmacophore hypothesis as the query
  • Improve speed of Quick Shape calculations with -limit and -NJOBS1D options that enable more efficient utilization of compute resources

Glide

  • Full release of Glide WS mode, previously known as WScore, to prioritize ligands for improved hit enrichment and pose prediction accuracy
    • Leverages explicit water energetics to enhance the accuracy of protein-ligand poses and reduce experimentally inactive compounds in top-scoring virtual hits

Lead Optimization

FEP+

  • Gain deeper insights into receptor-ligand interactions with new Per-Residue Energy Decomposition in FEP Edge analysis
  • Perform categorical analysis in the Correlation Plot (FEP+) interface using classification matrices including common metrics such as Accuracy, Specificity, Recall, Precision, F1 Score, Cohen’s K and Kendall’s T
  • View reason why compounds are skipped in ABFEP calculations in the FEP+ Panel

Protein FEP+

  • Full release of Protein FEP+ Residue Scanning (with lambda dynamics)
    • Workflow now generates an FMP archive to be loaded in the FEP+ Panel
    • Web Services support

FEP+ Protocol Builder

  • Ability to run with either OPLS4 or OPLS5 force field
  • Added support for sampling of more residue protonation states
  • New option to skip active learning and perform exhaustive exploration of protocol parameter space

Quantum Mechanics

  • Return solvation entropy in implicit solvent calculations
    distributed_frequencies.py workflow for numerical frequency calculations
  • Added support for isotope 11B in NMR calculations
  • Implicit solvent model SMD now has gradients and frequencies
  • E-sol now supports the CPCM-X implicit solvation method for rapid solvation energies (command line only)

Semi-Empirical Quantum Mechanics

  • Updated xTB to version 6.7.1 which uses the advanced solvent model CPCM-X

Macrocycles

  • Expanded list of predefined linkers from 5 to 22 for small molecule cyclization in macrocycle.py
  • Expanded list of side-chain bridges for peptide cyclization from 4 to 21 of the most commonly reported in the literature
  • Control spacers in macrocyclize.py via a CSV file of SMILES strings
  • Improvements to macrocycle alignment reproducibility and performance in tug_align script and Ligand Alignment Panel
  • Macrocycle sampling script can optionally output only macrocycle conformers
  • Easily create cyclic peptides from sequence on the command line with peptide_cyclize.py script

De Novo Design

AutoDesigner – R-group Design

  • Boost exploration of similar ligands with new AutoDesigner Similarity feature that scores output ideas based on similarity to a user-provided set of compounds
  • Added exhaustive PathFinder enumeration of all routes of the starting ligand using all available building blocks for those routes
  • Added recursive trimming of the final set of outputs to generate additional outputs
  • Improved logging including an overview of the number of compounds generated at various stages of the workflow

AutoDesigner – Core Design

  • Improved logging including an overview of the number of compounds generated at various stages of the workflow

Biologics Drug Discovery

  • Predict protein properties with automated machine learning model building using protein descriptors: Leverage AutoQSAR analysis to train, validate, and apply AI/ML models for biologics properties prediction
  • Predefined selection sets for quick access to TCR regions like CDRs, alpha/beta chains, and more

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Phonon-dependent dielectric properties reported in the Phonon DOS viewer
  • Workflow action menu (WAM) for NMR calculations
  • Support for phonon calculations with DFT-D3
  • Improved cell relaxation protocol
  • Schrödinger-compatible Quantum ESPRESSO releases available at Github
  • Support for distributed phonon calculations
  • Control over maximum number of retries after failure via config file (command line)
  • Initial parameters and constraints preserved in the QM Convergence Monitor

KMC Charge Mobility

Product: MS Mobility

  • Compute KMC Charge Mobility: Improved speed with robust QM convergence (command line)

Materials Informatics

Product: MS Informatics

  • Formulation ML: Increased number of available steps for hyperparameter tuning
  • Formulation ML: Option to replace hyperparameter tuning steps with training time
  • Formulation ML: Visualization of atomic contributions from the feature importance analysis
  • Machine Learning Property: Updates to existing models
  • Machine Learning Property: Prediction of melting point for molecular solids
  • Machine Learning Property: Prediction of non-aqueous solubility of molecules

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Automated CG Mapping: Speed up for mapping large molecules
  • Automated CG Mapping: Particle types and the number of occurrences reported

Penetrant loading simulations

Product: Penetrant Loading (PL)

  • Penetrant Loading: Differentiation between pre-existing water and added water

Reactivity

Product: MS Reactivity

  • Nanoreactor: Option to set the width of the biasing potential
  • Reaction Workflow: Anharmonic zero point energy (ZPE) added to Project Table
  • Reaction Workflow: Support for enumeration on sites in rings
  • Reaction Workflow: Option to automate the swap fragment with enumeration
  • Reaction Workflow: Preview of reaction diagram at the setup

Microkinetics

Product: MS Microkinetics

  • Microkinetic Modeling: Support for multistage MKM analysis
  • Microkinetic Modeling: Support for zoom in on plots in the viewer panel
  • Microkinetic Modeling: Increased default value for maximum integration time step

MS Maestro Builders and Tools

  • Adsorption Enumeration: Support for selection of reactive atoms by atom indices
  • Disordered System: Improved UI with reconfigured options for tabs and dialogs
  • Disordered System: Support for keeping selected molecules rigid with tangled-chain option
  • Meta Workflows: Support for radial distribution function analysis
  • Nanoparticle: Option to include only molecules with center of mass inside the particle

Classical Mechanics

  • Barrier Potential for MD: Option to remove barrier from input structures
  • Droplet: Support for entering random seed in building a droplet
  • Droplet: Support for randomized initial velocities
  • Evaporation: Support for full control over which profiles to plot
  • Evaporation: Support for applying barrier potentials
  • Evaporation: Option to set evaporation zone based on the distance from COM of the substrate
  • Evaporation: Improved loading speed for large input structures
  • MD Multistage: Improved relaxation protocol for ladder polymers
  • Refined default timestep for DPD particles
  • Thermostat and barostat settings set automatically for atomistic and coarse-grained systems
  • Stress Strain: Option to plot normal average stress
  • Thermophysical Properties: Option to return *.ene files (command line)
  • Trajectory Density Analysis: Output *.csv files

Quantum Mechanics

  • Adsorption Energy: Option to select between kcal/mol and kJ/mol for energy units
  • Adsorption Energy: All output entries incorporated in Project Table as subgroups
  • Adsorption Energy: Robust detection algorithm for valid input adsorbates
  • Adsorption Energy: Support for loading options from a Quantum ESPRESSO config file
  • Optoelectronic Film Properties: Prediction of molecular refractive indices
  • Optoelectronic Film Properties: Prediction of intersystem and reverse intersystem crossing rates
  • Optoelectronic Film Properties: Improved loading protocols for large input structures

Education Content

Life Science

  • New Tutorial: Protein pKa Prediction with Constant pH Molecular Dynamics
  • Updated Tutorial: Glide WS Evaluation of HSP90 Ligands

Materials Science

  • New Tutorial: Singlet-Triplet Intersystem Crossing Rate
  • New Tutorial: Modeling the Formation and Decomposition of Nitrosamines
  • New Tutorial: Atomic Layer Deposition
  • New Tutorial: Elemental Enumeration
  • New Quick Reference Sheet: Refractive Index
  • Updated Tutorial: Introduction to Multistage Quantum Mechanical Workflows

Docs Content

  • New documentation page to explore solutions for materials science applications and to identify the best fit for users’ interest
  • Panel images shown in the help topic of each panel

LiveDesign

What’s new in 2024-4

  • Biologics SAR Visualization: View, highlight, and analyze properties alongside sequence differences in the Sequence Viewer tool
  • Ligand Designer
    • Upload multiple overlays in the Ligand Designer Configurations via the admin panel, and enable or disable the overlays during design sessions
    • Rename ligands after editing and using the “predict pose” functionality to track and manage iterative design changes
  • R-group enumeration: Filter output products by computed properties
  • LiveDesign Learning: View the LiveDesign Learning Dashboard within Landing Pages
    • *LiveDesign Learning is now called LiveDesign ML

  • LiveReport Management
    • View row, column, and cell count for LiveReports in the LiveReport Picker
    • Set up a LiveReport as Read-Only or Hidden during creation in an updated Create LiveReport dialog
    • The following menu items have been consolidated into an “Edit LiveReport…” menu: Rename, Move to Folder, Make Read Only and Make Editable, Make Hidden and Make Visible
    • Admins can update Read-Only LiveReports to make them editable
    • Unhide a subset of compounds by clicking on a link in the LiveReport footer and selecting the compound IDs in a dialog
  • Documentation
    • The ? button in LiveDesign now directs to online documentation as is done elsewhere in the Schrödinger platform. Users will be directed to log into their Schrödinger web account that will be verified with a code to their email; if they do not have a web account, they will need to sign up for one.
  • Copy values from the Form spreadsheet, table, and ID widgets to the clipboard
  • Search and filter for a column name in the Create MPO dialog when adding a constituent column from the LiveReport
  • Click a link in a 3D cell for a 3D Generic Entity and Explicit Ligand Designer to view the 3D structure in Maestro
  • Entities appear in a LiveReport more quickly after a reaction enumeration, R-group enumeration, file upload, Maestro upload, and LDClient upload
  • LDClient’s API for retrieving FFC columns “get_freefrom_column_by_id” will now include an “updated_at” field containing a long corresponding to the timestamp that the FFC was last updated
  • Landing Page: View recent published experimental data from the Compound’s Page

What’s Been Fixed

  • Adding an entity to an Advanced Search, by searching for its ID, would show an unresponsive dialog, and now shows a dialog that accept an ID.
  • Complex filters would not accept a pasted biologic sequence, and would fail to filter to that sequence, but now accept pasted biologic sequences.
  • Creating a LiveReport from a template in the Landing Page would not open the newly created LiveReport, and now opens the LiveReport in a new browser tab.
  • If an entity is imported to multiple projects, purging the entity from one of the projects will make it disappear from that project only. Only one file entry now appears in the Manage Files Dialog for a multi-entity import through CSV, ZIP, and FASTA.
  • String type of entity relationship metadata is supported in DI by adding a DI mapping for relationship and relationship metadata.
  • The column “Structure Class” has been renamed to “Entity Type” in the Data & Columns tree
  • Pasting aromatic structures into the sketcher would flip chirality on structures containing a pyrrole, and now the chirality is maintained.
  • When entering FFC date values via LDClient, users must now use the YYYY-MM-DD format. If the date is entered in an incorrect format, the system will return a 400 error with the message: ‘Date must be in YYYY-MM-DD format.’
  • Forms kanban widgets would show a pin icon beside the kanban tile, and now pin icon does not appear in kanban widgets.
  • Unpinning a row now works as expected.
  • Model results with multiple values in a cell would show a different order of values if the LiveReport was duplicated, and now show the same order in the duplicated LiveReport as the origin LiveReport.
  • The Guanidine group in Arginine incorrectly displayed a carbon with five bonds in the 3D visualizer, and now accurately represents the chemistry, showing the correct bonding structure for Arginine residues.
  • Reordering of R-group scaffolds will work for newly added or deleted scaffold without page refresh in SAR analysis(R-group decomposition).
  • The deleted docked poses will no longer reappear, ensuring a clean and organized workspace. Each new pose generated after clicking “predict pose” will be sequentially named, allowing for easy tracking (e.g., “docked_ligand_4” following the deletion of “docked_ligand_3”).
  • Changing the model’s name via Admin panel will get correctly reflected in the Ligand Designer.
  • Targets would remain visible in the 3D visualizer after unchecking the display checkbox and then selecting a different entity, and now the Target display checkbox remains unchecked and the Target is not visible.
  • Copying a LiveReport from one project to another would grant the destination project ACLs to any model within the LiveReport, even if the Model’s Protocol did not provide access to the destination project. Now, the model column will be “dummified” and not visible in the destination project if the Protocol does not provide access to that project.
  • Models with dependent parameterized models will not show an error when archived, and all of its dependent parameterized models will also get archived. A dialog box will appear stating the number of dependent parameterized models that will be archived.

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Other Resources

Japan Webinars

Webinar Archive

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~第16回 Webinar Life Science
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~第16回

APR 16, 2025 | Accelerating protein degrader discovery: Computational strategies for degrader design and optimization

Virtual testing of personal care and cosmetics formulations using digital chemistry methods Webinar Materials Science
Virtual testing of personal care and cosmetics formulations using digital chemistry methods

FEB 19, 2025 | ケーススタディを通じて、計算化学が製品開発、容器設計、製品使用時の解析にどのように役立つかを示します。

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~ 第15回 Webinar Life Science
アーカイブ配信: OPLS5及び解離速度定数の予測技術のご紹介 Webinar Life Science
アーカイブ配信: OPLS5及び解離速度定数の予測技術のご紹介

DEC 18, 2024 | 本ウェビナーではOPLS5とunbinding kinetics workflowに焦点を当ててご紹介します。

アーカイブ配信: E-sol to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu Webinar Life Science
アーカイブ配信: E-sol to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu

NOV 13, 2024 |本ウェビナーでは、E-solの基本から応用事例、FAQまでを解説し、創薬研究におけるE-sol活用の可能性を探ります。

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~第13回 Webinar Life Science
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~第13回

OCT 17, 2024 | Broadly Impacting Biologics Discovery via Physics-Based Modeling: Antibody Affinity, pH-Sensing and More

Schrödinger Software 2024-3 新機能紹介ウェビナーアーカイブ配信 Webinar Life Science
Schrödinger Software 2024-3 新機能紹介ウェビナーアーカイブ配信

SEPT 3, 2024 | この度、最新版となる2024-3をリリースいたしました。本ウェビナーでは、主要な新機能についてご紹介いたします。

Case-study_MALT1-inhibitor Webinar Life Science
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~第12回

Accelerated In silico Discovery of SGR-1505: a Potent MALT1 Allosteric Inhibitor for the Treatment of Mature B-cell Malignancies

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~ 第11回 Webinar Life Science
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~ 第11回

強固なコンフォメーションのアンサンブル生成能力、受容体との相互作用を予測するための新しいドッキングワークフロー、ペプチド薬開発における特性空間の微調整の強化など、進化した構造ベースのツールを用いた事例を紹介します

 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~   Webinar Life Science
 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。

Schrödinger デジタル創薬セミナー:  Free Energy Calculations beyond Small Molecule Binding: Predicting Antibody Affinity, pH Sensing, Receptor Functional Response and More Webinar Life Science
Schrödinger デジタル創薬セミナー:  Free Energy Calculations beyond Small Molecule Binding: Predicting Antibody Affinity, pH Sensing, Receptor Functional Response and More

この講演では、FEP+メソッドの最近の拡張により、小分子結合以外の高付加価値のアプリケーションが能になった事例を紹介します

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

Case-study_MALT1-inhibitor Webinar Life Science
Japanese: 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~

Mucosa-associated Lymphoid Tissue Lymphoma Translocation Protein 1 (MALT1) is a genetically validated target for the treatment of diseases associated with lymphocyte regulation.

Japanese: 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~ 第2回 Webinar Life Science
Japanese: 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~ 第2回

Inhibitors of integrin αvβ6 have the potential to treat fibrotic disease through blockage of the TGFβ pathway.

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

アーカイブ配信: Efficient Prediction of Drug-Target Residence Time Using Enhanced Sampling Techniques Webinar Life Science
アーカイブ配信: Efficient Prediction of Drug-Target Residence Time Using Enhanced Sampling Techniques

リガンド結合における速度論は創薬においてますます重要であると認識されています。

Japanese: Schrödinger デジタル創薬セミナー Structure Based Drug Discovery without a Structure -Enabling Accurate FEP+ Predictions for Challenging Targets and ADMET Anti Targets Webinar Life Science
Japanese: Schrödinger デジタル創薬セミナー Structure Based Drug Discovery without a Structure -Enabling Accurate FEP+ Predictions for Challenging Targets and ADMET Anti Targets

During this webinar, we will showcase the successful utilization of unique technologies and dedicated workflows to enable accurate FEP+ predictions.

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.

The importance of human know-how in AI execution for R&D

The importance of human know-how in AI execution for R&D

How Schrödinger’s materials science domain experts ensure partner success

Overview

As artificial intelligence (AI) and machine learning (ML) technologies rapidly advance, materials scientists, executives, and R&D professionals are being tasked with developing an AI/ML strategy to drive innovation. Yet while AI/ML tools may advertise the appeal of push-button innovation, this is almost never the reality. Even the term AI itself can be a misleading buzzword.

Schrödinger is uniquely positioned to partner with materials R&D teams to execute AI/ML strategies that deliver true business value because we leverage the proven accuracy of physics-based modeling, the speed and scale of machine learning, and the deep domain expertise of our materials scientists. So while AI/ML technologies can be transformative, gaining a competitive advantage with AI is only possible with the talent, vision, and know-how of people who can utilize and direct these technologies towards meaningful, impactful outcomes.

Personalized human support is critical for project efficiency and productivity

Schrödinger’s professional software support is unique in providing domain expertise, personalized assistance, and reliable service. This human difference ensures that users can fully leverage the potential of digital tools to address complex issues through tailored guidance and support.

Schrödinger Support Benefits
24-7 global support from a team of experts Faster project timelines
On-site and virtual assistance with expert application scientists Technical discussions, troubleshooting, and knowledge transfer
Extensive online training, tutorials, and resources Skilled users to maximize outcomes
Expert-led customized modeling service packages Novel state-of-the-art research, knowledge transfer and improved success rates
“As a former industrial modeler at Boeing, I understand that the needs of each client are unique. That’s why we focus on delivering personalized support that addresses their specific challenges in R&D digitization.”
Andrea Browning Director of Polymers and Soft Matter
Andrea Browning
Director of Polymers and Soft Matter

Dr. Andrea Browning is leading the efforts related to polymer and soft matter simulation at Schrödinger. Prior to joining Schrödinger in 2017, she was a lead research engineer and project manager at The Boeing Company. She brings over a decade of experience in connecting simulations to industrial decisions, and has helped industry innovators on challenging projects such as developing bio-based polymer formulations, optimizing polymer matrix in carbon fiber composites, and designing electrolyte molecules.

Schrödinger technical experts increase capacity and capability

Through strategic partnerships or customized contract research, Schrödinger’s team of expert scientists work closely with customers to tackle challenging problems by deploying digital chemistry strategies to guide rapid materials design and optimization. By working as a team, we share the same challenges and goals. These expert-driven collaborations transform challenges into opportunities, driving innovation and delivering exceptional results.

“Over the course of 20 years working with companies in this sector, I am convinced that collaboration is the key to delivering value, by listening to industry needs and tailoring the modeling solution.”
Simon Elliott Director of Atomic Level Process Simulation
Simon Elliott
Director of Atomic Level Process Simulation

Dr. Simon Elliott is a pioneer in applying atomic-scale models to the chemistry of thin film material deposition and etch. In this field he has chaired international conferences, coordinated transnational networks and authored about 100 peer-reviewed papers. He is recognized in the semiconductor industry for his work with chemical companies on design of precursor gases and with equipment suppliers on optimizing atomic layer deposition processes. He was the 2023 recipient of the ALD Innovator Award.

Expertise and industry-grade software can be the difference between project success and failure

It is common for materials science researchers to piece together a variety of modeling and simulation tools. Free software packages are often limited by outdated documentation, insufficient support, and obstacles to integration and automation. The human element is one of the key differentiators between industry-grade software like Schrödinger and less sophisticated software packages.

Schrödinger has a large team working on user experience, technical support, and education. This team ensures that clients can seamlessly access the science underpinning the simulations and use it at a scale that allows them to get their job done.

“Having used free software extensively in the past, I now realize how much time I was spending on troubleshooting rather than actual research. Schrödinger’s solutions are a game-changer for productivity.”
Pavel Dub Senior Principal Scientist and Product Manager
Pavel Dub
Senior Principal Scientist and Product Manager

Dr. Pavel Dub is an expert in Catalysis & Reactivity. With a robust foundation built through dual doctoral studies in Russia (2009) and France (2010), followed by postdoctoral research at the Tokyo Institute of Technology and Los Alamos National Laboratory, Pavel’s research has evolved from experimental organometallic chemistry and homogeneous catalysis to computational chemistry and materials science, utilizing both classical and quantum computing platforms. Before joining Schrödinger in 2022, Pavel served as a Staff Scientist at Los Alamos National Laboratory, where he led the development of quantum algorithms for solving complex chemical problems. He has worked with clients on advanced topics, including molecular catalyst design, automated reactivity screening, and reaction network generation.

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

SEP 27, 2024

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

The simulation of materials properties using physics-based approaches, such as density functional theory (DFT) and molecular dynamics (MD), has long been successful in providing insights into structure-property relationships and subsequently aiding in the design of novel materials. In recent years, AI/machine learning (ML) has been used extensively in conjunction with physics-based modeling techniques to greatly accelerate materials innovation. The accuracy and generalizability of physics-based modeling improves the performance of AI/ML models and enables them to be used effectively even in small-data regimes. Conversely, the speed and flexibility of AI/ML help bridge the time- and spatial- scale limitations of physics-based models, creating a synergistic approach that optimizes both predictive accuracy and computational efficiency.

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.

Key Learning Objectives:

  • Understand how DFT descriptors enhance the accuracy of AI/ML models for optoelectronic molecules and battery electrolytes
  • Discover how MD simulation descriptors improve AI/ML models for the viscosity of organic molecules
  • Explore the use of Schrödinger’s automated Formulation Machine Learning solution to:
    • Train AI/ML models for the solubility of APIs in binary solvents
    • Predict the motor octane number of hydrocarbons
  • Learn about advances in AI/ML force field technology (QRNN) and its application in modeling the bulk properties of inorganic cathode coating materials

Our Speaker

Anand Chandrasekaran

Senior Principal Scientist, Schrödinger

Anand Chandrasekaran joined Schrödinger in 2019 and he is currently the Product Manager of MS-Informatics. His expertise is in applying machine learning to different areas in Materials Science and computational modeling. He graduated from the group of Prof.Nicola Marzari in the Swiss Federal Institute of Technology, Lausanne with a PhD in Materials Science. Before joining Schrödinger, Anand also worked in the group of Prof. Rampi Ramprasad on a number of topics including polymer informatics, machine-learning force-fields, and machine-learning for electronic structure calculations.

Designing quality ligand libraries

Designing Quality Ligand Libraries_Hero image

Designing quality ligand libraries


Exploring chemical space, profiling and tailoring ligand libraries, validating docking models, and methods of enumeration for hit discovery

Details
Modules
5
Duration
5 weeks / up to 20 hours
Level
Intermediate
Cost
$680 for non-student users
$255 for student / post-doc
Course Timeframe
Self-guided over five weeks. When registering for the course you will select the start and end date. Within those dates, you will have asynchronous access to the course material and virtual workstation to work on the course when it best suits your schedule.

With the substantial expansion of both virtual and physical ligand libraries, there is growing interest in filtering and profiling these libraries to be target and project specific prior to using them for hit discovery workflows such as high-throughput virtual screening.

Schrödinger’s online course, Designing Quality Ligand Libraries will teach library design best practices, as well as how to prepare and use ligands for model validation. 

This course is ideal for those who wish to develop professionally and expand their CV by earning certification and a badge.

  • Work hands-on with Schrödinger’s industry-leading Maestro and command line interface
  • Jump start your research program by learning methods that can be directly applied to ongoing projects
  • Learn topics ranging from reaction-based enumeration to library profiling
  • Independently perform a 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 within the course session

 

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
  • Working knowledge of Maestro. This course will not teach you how to navigate the Schrödinger graphical user interface, Maestro. Please work through our Getting Started with Maestro resources to become familiar with using Maestro.
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

Fundamental concepts of real and theoretical chemical space

Profile vendor libraries to assess their composition and perform various enumeration exercises to generate in-silico enumerated libraries

The library design process

Learn strategies and considerations of filtering ligand libraries to remove clear liabilities while maintaining diversity

Enrichment calculations for model validation

Learn popular decoy generation tools as well as various alternate approaches to build appropriate decoy/unknown ligands for your research project

Prospective applications of Library Design for HTVS

Apply your skills by independently preparing a ligand library to meet target and project-specific requirements

Modules

Module 1
2 Hours

The value of quality ligand libraries for high throughput virtual screening

Checkpoint
Syllabus and honor code

Expectations surrounding academic integrity

Video
Videos
  • Course overview
  • Virtual screening overview videos and the importance of quality ligand libraries for virtual screening workflows
End checkpoint
End of module checkpoint
Module 2
6 Hours

Generating chemical space and using commercial ligand libraries

Video
Video
  • Methods of generating chemical space
  • Understanding commercial libraries and the library design process
Tutorial
Tutorials
  • R-group and reaction-based enumeration
  • Library profiling
End checkpoint
End of module checkpoint
Module 3
4 Hours

Filtering and preparing ligand libraries

Video
Video
  • Strategies of filtering ligand libraries
  • Ligand preparation for virtual screening and HTVS next steps
Tutorial
Tutorials
  • Perform substructure and liability filtering
  • Perform LigPrep and prepare for shape screening
End checkpoint
End of module checkpoint
Module 4
4 Hours

Building ligand libraries for model validation

Video
Video
  • The purpose of ligand libraries for model validation
  • Other methods of generating decoy ligand sets
Tutorial
Tutorial

Generate, filter, prepare, and apply a decoy ligand set for enrichment analysis

End checkpoint
End of module checkpoint
Module 5
6 Hours

Final case study: Design a ligand library independently

Video
Videos
  • Case study overview
  • Case study findings and course closing
Tutorial
Tutorial

Generate, filter, prepare, and assess a ligand library for a new project

Assignment
Assignment

Review and discuss case study findings

Course completion
Course completion and certification

Need help obtaining funding for a Schrödinger Online Course?

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

“The course was useful because I learnt extra concepts not covered in the introductory course such as profiling and filtering ligands…, as well the criteria for identification of Hits from large libraries.”
Freebown RwereInstructor at Stanford University School of Medicine
“Taking the HTVS course was a great experience…My confidence also increased after completing the training. Not only did I learn a lot, but I also applied it to my dissertation.”
Santosh BasnetGraduate Student at Tribhuvan University
“This course was a fantastic introduction to why it’s important to prepare and prescreen your virtual libraries prior to running a HTVS. It has helped my team understand what is happening on the ground when working with our customers, which will facilitate better support. This course is also helpful as we develop our own virtual libraries to think about the compounds which should be included.”
Deren_Koseoglu headshot
Deren KoseogluVice President of Sales, eMolecules
“This was an excellent course to learn how to identify hits with drug-like properties from large libraries. I have enjoyed applying the knowledge in my Case Study and am looking forward to using the knowledge in my research.”
Kate KostenkovaStudent at University of Minnesota
“It is wonderful to have a group of high-quality professors teaching you computational chemistry. I think that the creation of ligand libraries in silico is an extremely useful skill in many circumstances. As a computational biologist it is gratifying to have this skill in my curriculum. Very grateful for the whole process, as always Schrödinger the best.”
Andres. R. Ch. PradaBiologist. M.Sc. Biostatistics. M.Sc. Computational Biology. Master in Molecular Biology, Andes University
Life science course badge

Show off your newly acquired skills with a course badge and certificate

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 does the Library Design online course cost?

Pricing varies by each course and by the participant type. For students wishing to take this, we offer a student price of $255, and $680 for non-students.

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

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Computationally-Guided Drug Formulation Webinar Series

Webinar

Computationally-Guided Drug Formulation Webinar Series

CalendarDate & Time
  • September 11th – November 6th, 2024
LocationLocation
  • Virtual

A smart, strategic drug formulation can efficiently advance your drug development projects and inform downstream processes. Advances in molecular modeling and machine learning are enabling atomistic-level insights and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.

Join us this fall for Computationally-Guided Drug Formulation Webinar Series – five webinars in which we will explore how the latest computational modeling tools are impacting the various steps in the pharmaceutical formulation process. In each webinar we will feature an expert from Schrödinger sharing valuable insights and practical applications on a key topic. Register for the series to learn how to optimize your formulation process with structure-based insights and efficient parameter screening.

  • September 11, 2024
    Characterizing small drug-like molecules with automated computational spectra prediction
    Speaker: Art Bochevarov, Research Leader
    Watch now
  • September 25, 2024
    Computational reactivity and catalysis for drug synthesis
    Speaker: Michael Rauch, Associate Director, Materials Science
    Watch now
  • October 9, 2024
    Molecular-level insight into solubility-enhancement via cosolvents and amorphous solid dispersions
    Speaker: Ben Coscia, Principal Scientist
    Watch now
  • October 23, 2024
    Crystal structure prediction workflow for small molecule drug formulation
    Speaker: Lingle Wang, Sr. Vice President, Scientific Development
    Watch now
  • November 6, 2024
    Modeling lipid nanoparticles: Self-assembly and apparent pKa calculation
    Speaker: John Shelley, Fellow
    Watch now
  • April 8, 2025
    Accelerating pharmaceutical formulations using machine learning approaches
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  • May 14, 2025
    Computational insights into polymer excipient selection for amorphous solid dispersions
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    Register

Computational Catalysis at Schrödinger

AUG 6, 2024

Computational Catalysis at Schrödinger

The CSIR in South Africa hosts a national academic license to many of Schrödinger’s modeling software.  Use of these molecular modeling tools are free to registered academics in South Africa.  This webinar will highlight the digital simulation tools specifically for Catalysis & Reactivity.  The topics covered will include:

  • Homogeneous Catalysis

  • Heterogeneous Catalysis

The presentation will also explain how to gain access to these tools, what are the free training options, and details on certified intensive training options.

Our Speaker

Pavel Dub

Senior Principal Scientist, Schrödinger

Pavel A. Dub serves as a Senior Principal Scientist and Product Manager for Reactivity & Catalysis at Schrödinger, Inc. He holds a PhD in Chemistry from the A. N. Nesmeyanov Institute of Organoelement Compounds, as well as a second PhD from the Université de Toulouse. Following two postdoctoral fellowships at the Tokyo Institute of Technology and the Los Alamos National Laboratory, where he later held a position as Staff Scientist, Pavel A. Dub joined Schrödinger, Inc. in 2022. His research endeavors encompass computational chemistry across classical and quantum architectures.

MS Reactive Interface Simulator

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries

MS Reactive Interface Simulator

Overview

MS Reactive Interface Simulator enables rapid modeling of solid electrolyte interphase (SEI) nucleation and growth in batteries using a template-based reaction approach, and offers atomistic insights into the composition and morphology of this complex battery component. Coupled with Desmond, Schrödinger’s high-speed GPU-based molecular dynamics (MD) engine, and the OPLS force field, MS Reactive Interface Simulator facilitates efficient analysis of electrolyte chemistries by generation of realistic SEI morphologies.

Key Capabilities

Check mark icon
Accelerate physically realistic SEI formation with GPU-accelerated MD
Check mark icon
Execute reactions using predetermined templates
Check mark icon
Enable exploration of multiple chemistries under varying conditions with SMARTS based reaction templates
Check mark icon
Employ advanced analysis tools to characterize morphology and understand the properties of the SEI layer

Related Resources

Electrodes, electrolytes & interfaces: Harnessing molecular simulation and machine learning for rapid advancements in battery materials development

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

Energy Capture and Storage

Related Products

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

Jaguar

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

MS Maestro

Complete modeling environment for your materials discovery

MS Reactivity

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

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules

Broad applications across
materials science research areas

Get more from your ideas by harnessing the power of large-scale chemical exploration
and accurate in silico molecular prediction.

Polymeric Materials
Catalysis & Reactivity
Energy Capture & Storage

Publications

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

Materials Science

Conformers influence on UV-absorbance of avobenzone

Materials Science

Synthesis, computational studies and evaluation of benzisoxazole tethered 1,2,4-triazoles as anticancer and antimicrobial agents

Materials Science

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies

Materials Science

Modified t-butyl in tetradentate platinum (II) complexes enables exceptional lifetime for blue-phosphorescent organic light-emitting diodes

Materials Science

Insights into the binding mechanism of 2,5-substituted 4-pyrone derivatives as therapeutic agents for fused dimeric interactions: A computational study using QTAIM, dynamics and docking simulations of protein–ligand complexes

Materials Science

Self-Assembled Tamoxifen-Selective Fluorescent Nanomaterials Driven by Molecular Structural Similarity

Materials Science

Accurate quantum chemical reaction energies for lithium-mediated electrolyte decomposition and evaluation of density functional approximations

Materials Science

Chemical reaction networks explain gas evolution mechanisms in Mg-Ion batteries

Materials Science

Machine learning force field ranking of candidate solid electrolyte interphase structures in Li-ion batteries

Materials Science

Elementary Decomposition Mechanisms of Lithium Hexafluorophosphate in Battery Electrolytes and Interphases

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.

Release 2024-3

Library Background

Release Notes

Release 2024-3

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Create customizable histograms from numerical data that are automatically synchronized with selection or filtering in other charts, the Project Table, or Workspace
  • Improved support for T-Cell Receptors with display of their annotations in the Structure Hierarchy

Force Field

  • Full release of the OPLS5 polarizable force field for organic atoms for improved FEP+ and Desmond model accuracy

Workflows & Pipelining [KNIME Extensions]

In LiveDesign:

  • Ability to use a single generic protocol regardless of model input columns
  • LiveDesign connection node can take credentials from the session rather than storing them in the workflow
  • Date type columns are supported as LiveDesign model input

Binding Site & Structure Analysis

SiteMap

  • Enable compact mode for sites with volume larger than a cutoff
  • New RNA mode for improved performance of SiteScore for RNA

Desmond Molecular Dynamics

  • New Unbinding Kinetics workflow to gain insights into drug-target residence time and optimize in vivo efficacy, safety profiles, and ADMET (beta)
  • Analyze halogen bonds in SID Panel
  • View local strain energy in “Torsion” tab of SID Panel

Mixed Solvent MD (MxMD)

  • Improved organization of output structures and data in prjzip file

Hit Identification & Virtual Screening

  • Streamline visualization of hits in the Hit Analyzer by outputting VSDB per docking run by default
  • Streamlined generation of WScore models with new WScore Quick Model Generation panel (beta)

Ligand Preparation

Hit Analysis

  • Filter chemotypes by SMARTS in Hit Analyzer Panel

FEP+

  • Improved management of pKa/tautomer/conformer ensembles on ABFEP systems with Groups tab
  • Core-SMARTS selection no longer requires selecting explicit hydrogen atoms
  • Improved user interface allows more intuitive column sorting
  • Export to LiveDesign now includes additional fields
  • Edge analysis now includes halogen protein-ligand interactions
  • Guided access to open FEP+ Panel for analysis upon calculation completion via Workflow Action Menus (WAM) in Maestro

Protein FEP

  • New lambda dynamics (λD) enhanced protein residue mutation FEP+ for identifying high quality protein variants (beta)
  • Expanded OPLS5 support for “Protein FEP” and “Protein FEP for Ligand Selectivity” panels

Solubility FEP

  • Expanded OPLS5 support for Solubility FEP simulations

FEP Protocol Builder

  • Gain up to 35% speedup in calculations due to changed defaults in the FEP Protocol Builder panel

Biologics Drug Discovery

  • Perform DNA/RNA nucleobase mutations using residue scanning on command line via mut-pred.py
  • Analyze DNA/RNA interactions with proteins in the Protein Interaction Analysis panel
  • Search the non-standard residues library and find the closest matching natural amino acid analog
  • Automatically annotate and number T Cell Receptor (TCR) structures using IMGT or AHo schemes
  • Use pose-viewer files as input for Protein Interaction Analysis

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Check for the number of irreducible k-points from the panel
  • Upgrade to Quantum ESPRESSO 7.3.1
  • Quicker assessment of electric field for faster phonon calculations
  • Force and stress information reported in the project table
  • Option for more diagonalization algorithms for GIPAW steps (command line)
  • Option to set separate driver and subjob hosts for NEB calculations
  • Solid State NMR Viewer: Improved UI for selecting elements

Transport Calculations via MD simulations

Product: MS Transport

  • Diffusion: Support for non-orthorhombic systems as input

Materials Informatics  

Product: MS Informatics

  • Formulation ML: Option to use Machine Learning Property predictions as descriptors
  • Formulation ML: Option to use DeepAutoQSAR predictions as descriptors
  • Machine Learning Property: Updates to existing models
  • Machine Learning Property: Prediction of S1-T1 energy gap
  • Machine Learning Property: Prediction of aqueous solubility
  • Machine Learning Property: Output entries separated for each solvent

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Coarse-Grained Force Field Builder: Automated mapping for dissipative particle dynamics (DPD)
  • Coarse-Grained Force Field Builder: Visualization of CG mapping in the workspace

Reactivity

Product: MS Reactivity

  • Nanoreactor: Frames from MD trajectory added to list of products
  • Nanoreactor: Support for multistate (e.g. singlet-triplet) reactions
  • Nanoreactor: Number of loaded structures reported in the viewer
  • Nanoreactor: Plot for reactants (red) shown with products (blue) in the viewer
  • Nanoreactor: Reactant structures to be included as standard output
  • Reaction Workflow: Support for AutoTS output as input

Microkinetics

Product: MS Microkinetics

  • Microkinetic Modeling: Support for renaming of reactions and participating species
  • Microkinetic Modeling: Automatic population of molecular weight for gas/solute species
  • Microkinetic Modeling: Automatic assigning of collision factor based on reaction type

MS Maestro Builders and Tools

  • Solvate System: Option to neutralize systems with built-in counterions

Classical Mechanics

  • Barrier Potential for MD: Support for NPT ensemble
  • Elastic Constants: Option to reset the viewer panel
  • Meta Workflows: Support for trajectory-based free volume analysis
  • Order Parameter: Option to compute acentric order parameter
  • Polymer Crosslink: Option to use a barrier potential
  • Polymer Chain Analysis: Support for molecules with less than 40 atoms

Quantum Mechanics

  • Adsorption Energy: Option to constrain atomic positions for systems with PBC
  • Optoelectronic Film Properties: Workflow solution encompassing transition dipole moment orientation and singlet excitation energy transfer (SEET) calculations

Education Content

Life Science

  • New Tutorial: Introduction to MD Trajectory Analysis with Desmond
  • New Tutorial: Re-scoring Docked Ligands with MM-GBSA
  • Updated Tutorial: Understanding and Visualizing Target Flexibility
  • Updated Tutorial: Approximating Protein Flexibility without Molecular Dynamics

Materials Science

  • New Tutorial: Singlet Excitation Energy Transfer
  • New Tutorial: FEP Solubility
  • New Tutorial: Genetic Optimization
  • New Tutorial: Adsorption of Panthenol on Skin with All-Atom Molecular Dynamics
  • Updated Tutorial: Applying Barrier Potentials for Molecular Dynamics Simulations
  • Updated Tutorial: Automated Dissipative Particle Dynamics (DPD) Parameterization
  • Updated Tutorial: Design of Asymmetric Catalysts with Automated Reaction Workflow
  • Updated Tutorial: Machine Learning Property Prediction
  • Updated Tutorial: Crosslinking Polymers

LiveDesign

What’s new in 2024-3

  • New LiveDesign Learning module for rapid AI/ML molecular property predictions: Enables highly scalable, automated AI/ML pipelines for drug design
    • *LiveDesign Learning is now called LiveDesign ML

  • Accelerated scaffold and R-group design with AutoDesigner Core Design: Automatically generate and optimize novel cores and R-group(s) simultaneously
  • Delete Published Freeform column and Formula columns from the Data & Columns Tree
  • Biologics:
    • Sequence-activity relationships in the Sequence Viewer:
      • Ability to add a quantitative column from the LR in the viewer
      • Correlate the changes in the residues and the activity data with the heatmap
    • There would only be one option when trying to import the Biologics data via csv and the option “Import As Single Entity for CSV” won’t show now.
    • Performance of structure hierarchy loading and item selection through hierarchy panel in the 3D Visualizer are improved.
    • Double-clicking an item in the hierarchy zooms to that selection in the 3D Visualizer workspace.
    • Set gap penalties in the sequence viewer to generate more useful alignments
  • Landing Pages:
    • The Landing page now links to a specific URL and enable bookmarking the Landing Page in a browser
    • Download resources and files from the Landing Page Resource page
  • Spreadsheet View:
    • A warning message alerting the user to expect decreased performance now appears on LiveReports that contain more than one million cells
    • Entity images no longer enlarge when hovering over the image, and can now be zoomed by clicking a magnifying glass button that appears to the right of the entity image
  • The User details page in the Admin Panel now shows a warning that unlicensed usernames will not appear in dropdown lists throughout LiveDesign
  • Models now support date and datetime returns
  • Forms Matrix Widget now render larger editing areas for Freeform column cells when the cells are small

What’s Been Fixed

  • LiveReports would occasionally lose their filters, and the filter panel would appear blank, but no longer lose their filters
  • Changing a user’s role within a Single Sign-on Identity Provider would not update the user’s role within LiveDesign when they logged out and logged back in, and now the role changes are correctly used after the user logs out of LiveDesign and logs back in
  • Changes to parameterized model in the Admin Panel (e.g., the Title or Folder) would not save after clicking the Save button, and now correctly save and update the parameterized model
  • Changes to a “set fixed” protocol parameter get passed along to the dependent model or parameterized model without breaking them.
  • The Formula Substructure Search function incorrectly reported the count of substructure matches as 1, even if there were multiple matches, and now correctly reports the total number of substructure matches
  • Adding a new project with an identical name to an archived project provided a cryptic error message, and now provides a clear message instructing the user to choose a different name
  • When many LiveReports were open, the active LiveReport tab would disappear when left-side panels were opened, and now the active LiveRepot tab remains visible
  • Newly created models would not inherit the Recalculate Model option defined in the protocol, and would default to the “Automatically” option, and now the models correctly inherit the option defined in the protocol
  • Parameterized models that have had their columns renamed in the Admin Panel would show the old, original column names when that model was added to LiveReports, and now correctly show the updated column name
  • The user interfaces of the Filters panel and Advanced Search panel have been unified
  • Changing the column widths within the LiveReport picker caused the column header to misalign with the column contents, and now the header remains aligned
  • The prefix (Global) would appear repeatedly for templates in the Global project that were updated and overwritten, and now templates in the Global project only show a single (Global) prefix after they are updated and overwritten
  • Maestro would not import 3D results from LiveDesign when the 3D column title was renamed, and now correctly imports all 3D data regardless of the column title
  • LiveDesign would occasionally freeze due to a database lock, and now no longer will freeze
  • Opening a model attachment from the main spreadsheet (e.g., a LID from a Glide model) would fail to show the image, and now correctly shows the image
  • Filtering out a frozen row would show flashing squares in the first row in the main spreadsheet, and now correctly shows that row’s data
  • The Project Picker would appear after a five-second delay when there are a large number of projects to show, and now the Project Picker appears instantly
  • The sequence viewer would occasionally show incorrect colors and tooltips for non-natural amino acids, and now shows the correct information
  • Hovering over a residue in the sequence viewer would cause the viewer to scroll to the top, and now the scroll position remains does not change
  • Plot tooltips could not be dragged and moved after pinning to the screen, and now can be dragged to a new position after pinning
  • Model results would occasionally appear as Failed in the LiveReport, when in fact the model ran successfully, and now model results correctly show results in the LiveReport

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

Homogeneous catalysis & reactivity

Catalysis_Hero

Homogeneous catalysis & reactivity


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

Details
Modules
6
Duration
6 weeks / ~25 hours to complete
Level
Introductory
Cost
$575 for non-student users
$150 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

Density functional theory

Learn to apply DFT for automated property prediction for organic and inorganic molecules

Reaction mechanism elucidation

Learn to leverage quantum mechanical workflows to predict reaction pathways and energetics

Machine learning

Learn to apply machine learning for rapid and accurate property prediction of organic molecules and catalytically active complexes

Modules

Module 1
2 Hours

Introduction to materials modeling

Video
Video

Introduction to materials modeling and this online course

Video Tutorial
Video tutorial

Introduction to materials science (MS) Maestro

Video
Video

Modeling for homogeneous catalysis and reactivity

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

Molecular quantum mechanics

Video
Video

Introduction to molecular quantum mechanics (mQM)

Tutorial
Tutorials
  • Functionals, basis sets and geometry optimizations
  • R-group enumeration
  • QM multistage workflows
  • Rigid and relaxed coordinate scans
  • Energies of reactions
  • Organometallic complexes
End checkpoint
End of module checkpoint
Module 3
6 Hours + Compute Time

Molecular quantum mechanics

Tutorial
Tutorials
  • Bond and ligand dissociation energy
  • Beta elimination reactions
  • Locating transition states: Part 1
  • Locating transition states: Part 2
  • Reaction workflow for polyethylene insertion
  • Nanoreactor
  • Design of asymmetric catalysts with automated reaction workflow
End checkpoint
End of module checkpoint
Module 4
3 Hours + Compute Time

Machine learning

Video
Video

Introduction to machine learning (ML)

Tutorial
Tutorials
  • Machine learning for materials science
  • Machine learning for homogeneous catalysis
End checkpoint
End of module checkpoint
Module 5
2 Hours + Compute Time

Guided case study

Tutorial
Tutorials
  • Fundamental organometallic reactivity
  • Combining AutoTS and reaction workflow
End checkpoint
End of Module Checkpoint
Module 6
4 Hours + Compute Time

Independent case study

Assignment
Assignment

Predicting regioselectivity of hydroboration

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

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Related Courses

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

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: Pharmaceutical Formulations Materials Science Materials Science
Pharmaceutical formulations

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

Supporting Associations

nanoHUB

MS Microkinetics

MS Microkinetics

Efficient tool for surface reaction kinetics

MS Microkinetics

Overview

MS Microkinetics is an effective tool for calculating the overall kinetics of a network of surface reactions, which can be used to optimize reaction conditions and to identify reactivity bottlenecks.

MS Microkinetics

Key Capabilities

Given the reaction mechanism (or multiple mechanisms) and activation free energies, MS Microkinetics can calculate:

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Reaction rates for the elementary reaction steps
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Reaction orders
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Degree of rate control
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Time-dependent and steady state coverages of the reactants, products, and intermediates
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Turnover frequency in the case of catalytic cycles
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Growth rate or etch rate in the case of deposition or etch processes

Efficient tools and solutions to predict activation energies

Quantum ESPRESSO GUI

Integrated graphical user interface for nanoscale quantum mechanical simulations

Learn more
MS Reactivity

Automatic workflows for accurate prediction of reactivity and catalysis

Learn more
AutoTS

Automatic workflow for locating transition states for elementary reactions

Learn more

Accelerate the design of high-performance heterogeneous catalysts

Efficient computational solutions leveraging atomic-scale simulation, machine learning, and enterprise informatics for catalytic reactions using solid-state catalysts.

Related Products

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

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 Maestro

Complete modeling environment for your materials discovery

MS Reactivity

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

Learn more about our solutions

Semiconductor
Catalysis & Reactivity
Energy Capture & Storage
Metals, Alloys & Ceramics

Training Tutorials & Courses

Microkinetic Modeling
View tutorial
Nanoreactor
View tutorial
Surface Chemistry
View course
Schrödinger Materials Science Online Courses
View courses

Documentation & Tutorials

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

Materials Science Documentation

Materials Science Panel Explorer

Quickly learn which Schrödinger tools are the best fit for your research.

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.

Pharmaceutical Formulations & Delivery

Pharmaceutical Formulations & Delivery

Deliver better medicines through in silico design

Optimize Drug Formulation Process

Optimize your pharmaceutical at the molecular level

A smart, strategic drug formulation can efficiently advance your drug development projects and inform downstream processes. Advances in molecular modeling and machine learning are enabling atomistic-level insights to improve drug formulations and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.

Schrödinger offers a range of computational solutions for advancing pharmaceutical formulation, from crystalline or amorphous form characterization to selection of materials and excipients for processing, formulation, and delivery.

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Intuitive computational workflows designed by experts in formulation chemistry

Benefit
Easy-to-use system builders for complex formulations of large molecular systems
Benefit
Powerful workflows for molecular simulation, machine learning, and data analysis
Benefit
Dedicated customer support and extensive training resources

Key Capabilities

Optimize drug process development and manufacturing with predictive characterization

  • Predict pKa, powder X-ray diffraction and crystal morphology 
  • Calculate Young’s and shear moduli to aid in the optimization of tableting conditions
  • Understand solubility in non-aqueous solvents
  • Simulate spectroscopy including VCD, NMR (solution and solid-state), IR, Raman, and UV-Vis

Understand drug stability and reactivity

  • Predict glass transition temperature and water uptake in amorphous materials, including amorphous solid dispersions
  • Evaluate drug stability with respect to various degradation channels
  • Calculate bond dissociation energy to evaluate chemical stability
  • Design molecular catalysts with automated solutions

Predict solubility of drug candidates

  • Accurately predict solubility of amorphous and crystalline forms to encourage the discovery of a soluble active pharmaceutical ingredient (API) and to delineate the potential solubility boost from non-crystalline forms using FEP+
  • Identify instances where pure drug solubility can exceed the expected solubility due to the formation of small drug aggregates

Characterize and optimize drug formulations and delivery

  • Gain insight into the complex requirements and behaviors of lipid-based and polymer-based formulations, including amorphous solid dispersions
  • Evaluate the impact of different polymers or polymer residues on the release solubilization and aggregation of the API
  • Predict key properties such as hygroscopicity, viscosity and miscibility of ingredients, molecular interactions in solution, and drug release profiles

Crystal Structure Prediction Services

De-risk your solid form selection process by identifying the most stable polymorph at room temperature

Overcome the risks associated with disappearing polymorphs in late stage drug development. For a given active pharmaceutical ingredient (API), we will leverage our proprietary crystal structure prediction (CSP) platform to identify the most stable crystal polymorph at room temperature. Starting from a 2D structure of the API, we deliver to you the thermodynamic stability ranking of crystal polymorphs.

Case Studies & Webinars

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

Featured courseMolecular Modeling for Materials Science: Pharmaceutical Formulations

Learn in silico drug formulation methods with our hands-on online certification course

Level-up your skills by enrolling in our online course, Molecular Modeling for Materials Science: Pharmaceutical Formulations.

Learn More

Documentation & Tutorials

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

Materials Science Tutorial

Nanoemulsions with Automated DPD Parameterization

Learn how to automatically build a coarse-grained force field for dissipative particle dynamics (DPD) from a nanoemulsions system with water and perform a molecular dynamics simulation.

Materials Science Tutorial

Umbrella Sampling

Learn to calculate the free energy profile for butanol permeation through a DMPC membrane using umbrella sampling.

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

Optimization of Formulations Using Machine Learning

Learn to build machine learning (ML) models to predict distinct properties of formulations and leverage these models to optimize formulations for desired target properties.

Materials Science Tutorial

Crystal Structure Prediction

Learn to perform a crystal structure prediction workflow.

Life Science Tutorial

Crystal Structure Prediction

Learn to perform a crystal structure prediction workflow.

Life Science Tutorial

Automated Martini Fitting for Coarse-Grained Simulations

Use the Coarse-Grained Force Field builder to automatically fit parameters for the Martini coarse-grained force field, utilizing all-atom systems as the reference for various systems.

Life Science Tutorial

Thin Plane Shear

Learn to calculate the thin plane shear viscosity and friction coefficient.

Materials Science Documentation

Materials Science Documentation

Comprehensive reference documentation covering materials science panels and workflows.

Materials Science Tutorial

Disordered System Building and Molecular Dynamics Multistage Workflows

Learn to use the Disordered System Builder and Molecular Dynamics Multistage Workflow panels to build and equilibrate model systems.

Key Products

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

Formulation ML

Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

MS Maestro

Complete modeling environment for your materials discovery

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

FEP+

High-performance free energy calculations for drug discovery

MS Morph

Efficient modeling tool for organic crystal habit prediction

MS CG

Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales

Jaguar

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

Crystal Structure Prediction

De-risk your solid form selection process by identifying the most stable polymorph at room temperature

Publications

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

Software and services to meet your organizational needs

Software Platform

Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecular modeling, design, and collaboration.

Modeling Services

Leverage Schrödinger’s computational expertise and technology at scale to advance your projects through key stages in the drug discovery process.

Support & Training

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