All Schrödinger offices worldwide will be closed for the week of August 17-21 as part of a company-wide initiative to rest and recharge. Please expect limited responses during this time. Scientific and Technical Support team members will be available to answer emergency support issues only.

Crystal Structure Prediction

Crystal Structure Prediction (CSP)

Crystal Structure Prediction (CSP)

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

Stability ranking of crystal polymorphs

Overcome the risks associated with disappearing polymorphs in late stage drug development. Schrödinger’s proprietary crystal structure prediction platform identifies the stable crystal polymorphs at 0K and RT for a given active pharmaceutical ingredient (API).

Key Capabilities

Fast and comprehensive identification of polymorphs at room temperature and beyond

  • Novel, systematic approach allows exhaustive yet efficient sampling of crystal packings
  • High throughput workflow with quick turnaround time to support CADD and CMC teams working on lead optimization (CSP for scaffold design), polymorph screening experiments (CSP-DoE), and manufacturing processes (CSP-derisk)
  • Includes advanced workflows for the prediction of anhydrous Z’=2, monohydrate, solvate, and salt forms of the API

High accuracy validated on extensive dataset of challenging, diverse drug-like molecules

  • Retrospective validation on a set of 66 drug-like molecules with 137 experimental polymorphs and an accuracy close to 100% in predicting the most stable solid form
  • Prospective validation on a central nervous system drug molecule showed high accuracy and reliability

Crystal Structure Prediction Workflow

Schrödinger solutions for physicochemical property prediction

Optionally predict key properties of an API to support selection of a stable solid form.

Crystalline solubility of polymorphs using free energy methods with FEP+
Learn more
ssNMR chemical shifts to support crystallization experiments with Quantum ESPRESSO
Learn more
Crystal habits (morphology) to support downstream processing with MS Morph
Learn more
Mechanical properties (Young’s and shear modulus) to complement direct compaction and milling experiments with MD engine Desmond

Learn more
Featured Service

Crystal Structure Prediction Services

Work with our team of computational experts to de-risk your solid form selection process. Starting from a 2D structure of the API, Schrödinger’s team will deliver to you the thermodynamic stability ranking of crystal polymorphs.

Publications

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

A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study

Zhou D, et al. Nature Communications, 2025, 16, 2210

Free energy perturbation approach for accurate crystalline aqueous solubility predictions

Hong RS, et al. J. Med. Chem. 2023, 66, 23, 15883-15893

Novel physics-based ensemble modeling approach that utilizes 3D molecular conformation and packing to access aqueous thermodynamic solubility: A case study of orally available bromodomain and extraterminal domain inhibitor lead optimization series

Hong RS, et al. J. Chem. Inf. Model. 2021, 61, 3, 1412-1426

Related Products

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

MS Maestro

Complete modeling environment for your materials discovery

FEP+

High-performance free energy calculations for drug discovery

Desmond

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

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

MS Morph

Efficient modeling tool for organic crystal habit prediction

OPLS4

Modern, comprehensive force field for accurate molecular simulations

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.

Lunch & Learn: Accelerating molecular discovery using an in silico design platform

Lunch and Learn

Accelerating molecular discovery using an in silico design platform

CalendarDate & Time
  • June 27th, 2025
  • 10:00 to 16:00 CET
LocationLocation
  • Basel, Switzerland
Register

Take a break from your other obligations and let’s talk! We are inviting you to join us on Friday, June 27th at the Hotel Essential by Dorint Basel City for an extended version of our Lunch and Learn series where we will introduce the diverse capabilities of Schrödinger’s molecular design platform. Schrödinger scientists will present how modern physics-based simulation methods as well as machine learning can help to create better molecules faster and answer your questions around computer-driven molecular design.

Date & Time:

Friday, June 27, 2025

From 10:00 AM to 16:00 PM CET

Program:

Part 1: Small Molecule

10:00 – 12:00

Accelerating small molecule drug discovery combining physics-based methods, machine learning techniques, and digital chemistry

David Rinaldo, Senior Principal Scientist, Applications Science

Drug design is a highly complex task literally consisting in finding a molecule with defined properties in the ocean of all possible molecules. In the past 10 years the field has experienced dramatic changes including an explosion in the size of commercial chemical libraries, a significant increase in available computing power (GPU, cloud computing), and significant advances in machine learning techniques. In this workshop, we will see how Schrödinger has been leveraging those technological revolutions to accelerate the small molecule drug discovery process.

First, we will present a few physics-based methods that Schrödinger has been recently developing and then we will see how those techniques can be combined to efficiently and collaboratively find new drug candidates.

+ Lunch

Part 2: Biologics

13:00 – 16:00

Accelerating biologics design through computational modelling and collaborative enterprise informatics

Dan Cannon, Principal Scientist II, Applications Science

Jelena Vucinic, Senior Scientist I, Applications Science

This Schrödinger workshop will explore how to use physics-based computational methods (BioLuminate, FEP+) and collaborative enterprise informatics (LiveDesign) to accelerate biologics design. We will present case studies on large-scale mutagenesis for affinity, analyzing and correcting developability issues, variant selection based on cross-reactivity, multi-parameter ideation, and more. Whether you’re new to computational tools or looking for a deeper dive into advanced methods, this session is designed to provide valuable insights for all experience levels.

+ Coffee and Cake

Location:

Hotel Essential by Dorint Basel City
Schönaustrasse 10, CH-4058 Basel
Click here to view map of room location.

Register

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

JUN 17, 2025

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

Artificial intelligence (AI) and machine learning (ML) are transforming materials science, unlocking new possibilities for innovation by enabling data-driven design and optimization across a wide range of applications. From accelerating the discovery of novel materials to optimizing formulations for specific performance criteria, AI/ML allows researchers to explore complex chemical spaces with unprecedented speed and precision. These approaches reduce reliance on trial-and-error experimentation, even in data-limited environments; empowering scientists to tackle challenges across diverse technology domains, including electronics, energy storage, polymers, and catalysis. Schrödinger’s integrated platform, which combines chemistry-informed ML with physics-based simulations, enhances predictability, scalability, and overall innovation in materials design.

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. Additionally, we will introduce the Schrödinger platform, illustrating how it empowers researchers to efficiently design and optimize materials. Specifically, we will highlight:

  • Design of novel OLED devices with physics-augmented machine learning
  • Optimization of materials properties for consumer packaged goods, battery electrolytes, polymers, and catalysis
  • Utilization of machine learning force fields (MLFF) for enhanced throughput and precision of atomistic simulations

Our Speaker

Anand Chandrasekaran

Senior Principal Scientist, Schrödinger

Anand Chandrasekaran joined Schrödinger in 2019 and currently serves as the Product Manager for MS Informatics. His expertise lies in applying machine learning across various domains within materials science and computational modeling. He earned his Ph.D. in Materials Science under Prof. Nicola Marzari at the Swiss Federal Institute of Technology, Lausanne. Prior to joining Schrödinger, Anand worked with Prof. Rampi Ramprasad, focusing on polymer informatics, machine learning force fields, and machine learning for electronic structure calculations.

Schrödinger Polymer Workshop 2025

Workshop

Schrödinger Polymer Workshop 2025

CalendarDate & Time
  • May 21st, 2025
LocationLocation
  • Mannheim, Germany
Register

Using Schrödinger’s Materials Science Suite for atomic-scale simulations

Schrödinger invites you to a one-day in-person workshop in Mannheim, Germany to gain hands-on experience with Schrödinger software for polymer design and simulation for a variety of applications.

Participants will get practical experience and in-person guidance in using our Materials Science Suite and the tools involved in building molecules, polymers, and complex mixtures for use in molecular dynamics simulations. Leveraging automated property prediction workflows as well as analysis tools will play an important role. Another aspect will be the application of machine learning.

Examples of how molecular-scale simulations can inform polymer and polymer formulation development will be included throughout the day.

Full agenda TBD

When & Where:

Wednesday 21st May 2025

Glücksteinallee 25
68163 Mannheim
Germany
(5 minutes walk from Mannheim Hauptbahnhof)

Please see our  page for information regarding what to bring, getting to the venue, and accessibility.

If you need further information please contact Patrick Heasman: patrick.heasman@schrodinger.com

If you are interested but unable to attend in person, please reach out to the contact above.

Registration:

Registration is free and includes lunch and refreshments.

Participants must bring their own laptop to access the software, and an external mouse is recommended. We will be utilising our Virtual Computer, which is accessed via web browser – No software installation is required prior to the session.

Places are limited, so please ensure to register as soon as possible.

Registration will close at latest on Friday 16th May 2025.

Who should attend:

Any researcher studying polymer design, polymer application, or generally interested in learning about computational materials science. No prior experience is required.

Instructional material can be reviewed before or after the workshop for free on our website:

 

Speakers and demonstrators:

  • Dr. Caroline Krauter
  • Dr. Irene Bechis
  • Dr. Patrick Heasman

Agenda

Register

FAQs

How long is the workshop?

The workshop is an all day event to give you the best opportunity to learn about our tools and benefit from the practical sessions throughout. We will start at 10:00 am and finish at approximately 4:00 pm.

Where is the venue and how can I get there?

The workshop is being help at our offices in Mannheim, Germany. The building is accessible via car, and the train station is within a 5 minute walk.

What is included with my registration?

Registration is completely free to attend the workshop. We will also be providing food and refreshments throughout the day.

Can I join the session virtually / remotely?

As we want to give the attendees help and guidance during the workshop we currently have no intention of running this workshop online. Please reach out if you are interested but are unable to travel to the event location.

Please contact Patrick Heasman (patrick.heasman@schrodinger.com) for any additional information about the event and the location.

Travel:

  • Via plane / train:
    Frankfurt / Frankfurt Airport – A direct train to Mannheim takes approximately 45 minutes.
  • Via car:
    There are several car parks located on Glücksteinallee.

Hotel recommendations:

  • Holiday Inn Mannheim City
  • LanzCarré Hotel Mannheim
  • Premier Inn Mannheim City Centre hotel
  • Hilton Garden Inn Mannheim

What do I need to bring?

A laptop is required for this workshop. We will not be providing any on the day, so please ensure that you bring one. We also recommend that you bring a personal laptop to avoid any firewall restrictions.

An external mouse is not required, but we do recommend that you bring one as our software makes full use of the 3 buttons.

Do I need to download and install the software prior to the event?

No. We will be utilising the Schrödinger Virtual Computer for all hands-on sessions. A suitable web browser is required for accessing this (Chrome, Edge, Firefox).

Release 2025-2

Library Background

Release Notes

Release 2025-2

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • New Welcome Screen on startup provides quick access to common tasks such as creating and opening projects and importing structures
  • Modernized and streamlined Project Table for enhanced usability
    • New Table Configuration pane allows fast switching between Light and Dark themes and toggles visibility of the ePlayer and Property Tree.
    • New Gadgets Menu provides convenient access to Charts and the 2D Viewer
  • New Workflow Action Menu (WAM) to view spectroscopy results from Jaguar and Jaguar Spectroscopy calculations in the Project Table

Target Validation & Structure Enablement

Protein Preparation

  • Improved minimization protocol to support broader coverage of biological and chemical systems
  • Produce more reliable prepared structures by expanded coverage of equivalent tautomeric ligand states
  • More easily view serious structural issues by filtering diagnostic reports with a severity threshold
  • New ‘Missing Atom’ tab on the Diagnostics panel enables select sidechain and loop modeling

Cryo-EM Model Refinement

  • GlideEM poses are now sorted by GlideScore which is more discriminating in ranking low RMSD structures than Denscore

Ligand Preparation

Ligand Docking

  • Faster Glide scoring and docking with optimized Glide (Beta): Screen larger libraries and find better candidates with optimized Glide, including enhanced Active Learning Glide and Python API support
    • Same industry-leading Glide docking funnel and scoring functions, Emodel and GlideScore
    • Faster turnaround with same compute resources for Active Learning Glide and AutoDesigner
    • Advanced Python API support offers easy automation and file control over docking process for greater experimentation
    • Accessible through the new Ligand Docking panel that enables setup of Active Learning and Glide calculations

ABFEP

  • Energy Decomposition data is now reported in Analysis PDF reports

Lead Optimization

FEP+

  • New FEP+ Pose Builder workflow for automatically generating high-quality ligand alignments (Beta): Generate FEP-ready poses faster and run FEP+ at scale with an automated workflow designed for unbiased selection and robust atom-mapping
  • Ability to read and write FEP+ Protocol files directly in the FEP+ Panel
  • Improved Classification matrix styling
  • Kendall’s tau statistic added to the statistical metrics reported
  • Improvements to exported FEP+ data in csv/xls formats
  • Added ‘None’ as a new Hot Atom Rule

Protein FEP

  • FEP+ Residue Scan supported in Protein FEP+ for Ligand Selectivity panel

Constant pH Simulations

  • Added support for Cysteine residues

FEP+ Protocol Builder

  • Sharply reduced compute resources to run default workflow by shrinking initial simulation times to 0.5 ns and extended times to 10 ns
  • Seamless interconnection as FEP+ Panel can now Read/Write Protocol Builder files
  • Bias the selection of protocols to extend including compute efficiency via Pareto analysis (command line only)
  • Added support for covalently bound ligands
  • Ability to optionally sample charge states of GLU, ASP, LYS, ARG, and CYS in protocol optimization

De Novo Design

AutoDesigner – R-group Design

  • New R-group Similarity score feature to focus ideation around compounds of interest
  • New Design Rationale capability to improve ADME endpoints with respect to reference ligands

Alternative Modalities

Bifunctional Degraders

  • Expanded support for protein degrader modeling with the new Degrader Sampling Workflow (Beta): Generate accurate degrader ternary complexes through integration of protein-protein docking and linker sampling in a structure-based workflow

Biologics Drug Discovery

  • Augmented AI/ML capabilities for biologics with machine learning-based T-Cell Receptor (TCR) structure prediction (Beta): Perform high throughput structure prediction and large scale modeling of TCRs with the ImmuneBuilder deep learning model and Prime
  • New Macromolecular Pose Filtering panel to filter native or near native poses from an ensemble of complexes using experimental data such as HDX-MS

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • A new environment variable for the location of Quantum ESPRESSO binary

Transport Calculations via MD simulations

Product: MS Transport

  • Thin Plane Shear: Selection of slab region by molecular units

KMC Charge Mobility

Product: MS Mobility

  • Compute KMC Charge Mobility: Predictions based on Schrödinger’s new mobility engine

Materials Informatics

Product: MS Informatics

  • Machine Learning Property: Updates to existing models
  • Machine Learning Property: Prediction of triplet reorganization energy
  • Machine Learning Property: Prediction of S1-to-T1 energy gap (∆EST)
  • Machine Learning Property: Predictions from the interactive mode automatically added to the Project Table
  • MLFF Calculations (Beta): Single-point energy and geometry optimization tool using Schrödinger’s latest machine-learned force fields

Formulation ML

Product: MS Formulation ML

  • Formulation ML: Support for custom ingredient descriptors
  • Formulation ML: Support for creating models using multiple CPUs in parallel
  • Formulation ML: Support for setting mixtures as individual components
  • Formulation ML Optimization: Workflow solution to optimize materials formulations

Layered Device ML

Product: MS Layered Device ML

  • OLED Device ML: Workflow solution to predict OLED device performance
  • Optoelectronic Device Designer: Use ML OLED device models to predict performance

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Automated CG Mapping: (+AUTOMAPPING_MARTINI_PROTEIN) Support for proteins in automated mapping and parameterization for Martini
  • Automated CG Mapping: Accurate mapping for carbohydrate systems
  • Improved threshold for momentum errors in CGMD simulations
  • CG FF Builder: Parameters for water-water interactions fixed by default

Dielectric properties

Product: MS Dielectric

  • Complex Permittivity: Option to run replicates in parallel

Reactivity

Product: MS Reactivity

  • Reaction Network category created under the Materials task menu
  • Reaction Workflow renamed to Reaction Network Profiler
  • Auto Reaction Workflow renamed to Reaction Network Enumeration Profiler
  • Reaction Network Profiler: Option to run conformational search using CREST
  • Reaction Network Profiler: Conformational search included in restarts (command line)
  • Nanoreactor: Option to screen products by energy relative to reactant state
  • Nanoreactor: (+ELEMENTARY_REACTION_NETWORK) Support for the Elementary Reaction Network workflow

Microkinetics

Product: MS Microkinetics

  • Microkinetics Deposition Analysis: Workflow solution to run post-analysis of microkinetic simulations in deposition or etch processes of solid materials
  • Microkinetic Modeling: (+MATSCI_MKM_INTERACTIONS) Support for simple quadratic adsorbate-adsorbate interactions

Reactive Interface Simulator

Product: MS RIS

  • Solid Electrolyte Interphase: Option to block intramolecular reactions (command line)
  • Solid Electrolyte Interphase: Option to use DFT charges for new species

Crystal Structure Prediction

Product: Crystal Structure Prediction

  • Crystal Structure Prediction: Interface and workflow to predict crystal structures and polymorphs for a given molecular compound

MS Surface

Product: MS SurfChem

  • Adsorption Enumeration: Access to workflow assessing reactive adsorption
  • Desorption Enumeration: Workflow solution for assessing desorption of multiple molecules

MS Maestro User Interface

  • Direct link from the task menus to Materials Science Panel Explorer page

MS Maestro Builders and Tools

  • Structured Liquid: Automatic standardization of custom lipids
  • Polymer: Improved dihedral setups for multiple shortest-length backbones
  • Organometallic Conformational Search: Option to run conformational search using CREST

Classical Mechanics

  • Evaporation: Option to export the results as CSV file
  • MD Multistage: Center of mass motion removed for coarse-grained systems
  • Thermophysical Properties: Option to save trajectory energy file
  • Umbrella Sampling (Beta): Workflow solution for umbrella sampling of membranes

Quantum Mechanics

  • Adsorption Energy: Support for reactive adsorption and desorption energies
  • Adsorption Energy: Improved assessment of entropy loss during the adsorption
  • Bond and Ligand Dissociation: Option to set product charges from formal atomic charges
  • Bond and Ligand Dissociation: Support for PCM and SMD solvent models
  • Bond and Ligand Dissociation: Improved 2D visualization of charges and radicals in product fragments
  • Crest: UI for semiempirical QM based conformational search using CREST
  • Optoelectronic Film Properties: Support for multiple reorganization energies as input for computing intersystem crossing (ISC) rate
  • Optoelectronic Film Properties Viewer: Support for user-input reorganization energies to instantly re-evaluate SEET rate
  • Thermochemistry Viewer: Support for viewing reactive adsorption and desorption energies
  • Trajectory Density Analysis: Improved naming scheme for atom groups

Education Content

Life Science

  • New tutorial: Exploring Protein Binding Sites with Mixed-Solvent Molecular Dynamics
  • New tutorial: Introduction to T-Cell Receptor Modeling with BioLuminate
  • Updated tutorial: Antibody Visualization and Modeling in BioLuminate
  • Updated tutorial: Peptide Modeling with BioLuminate
  • Updated tutorial: Target Analysis with SiteMap and WaterMap
  • New QRS: Structure Reliability Report
  • New QRS: Custom Reactions for Covalent Docking
  • New QRS: Mixed-Solvent Molecular Dynamics
  • Updated QRS: GlideWS Model Generation
  • Updated QRS: MM-GBSA Residue Scanning

Materials Science

  • New Tutorial: Umbrella Sampling
  • New Tutorial: Crystal Structure Prediction
  • New Tutorial: Optimization of Formulations Using Machine Learning
  • New Tutorial: Machine Learning for OLED Device Design
  • New Tutorial: Nanoemulsions with Automated DPD Parameterization
  • New Tutorial: Applied Machine Learning for Formulations
  • Updated Tutorial: Atomic Layer Deposition
  • Updated Tutorial: Design of Asymmetric Catalysts with Reaction Network Enumeration Profiler (previously AutoRXNWF)
  • Updated Tutorial: Machine Learning Property Prediction
  • New QRS: CREST
  • New QRS: Microkinetics Deposition Analysis

LiveDesign

What’s New in 2025-2

  • Import Antibody-Drug Conjugates from SimpleSchema
  • Freeform Columns
    • New “Comment” type: Write threaded conversations within a Freeform column, and track usernames and timestamps
    • Creating a Freeform column is now performed using a wizard that permits selecting the data type of the Freeform column first, and then specifying the Freeform column details
  • Formulas
    • Formulas support multiple values: Input cells to formulas can now contain multiple valuesFormulas can output multiple values, aligned by Experiment, Lot, or Pose. New formula functions were created to handle input cells with multiple values: cellAggregation(), join(), any(), and all()
    • Failed model cells can now be used as formula input
    • A new isFailed() function returns true for any failed model cells, and false otherwise
  • Dashboards (Previously called Landing Pages)
    • Biologics and Generic entities are now supported on Landing Pages
    • Project Admins can edit the project description directly on Landing pages instead of configuring it from the Admin panel
    • Project admins can now select a date range via the ‘Time range’ filter in Landing Page Entities Section to look for entities added within a range of days
    • A ‘Bookmark LiveReport…’ option has been added in the LR grid menu and clicking on the option redirects the user directly to the new Bookmark page (in Landing pages) in a new window. The LiveReport’s name gets auto-populated in the LiveReport info section of the window
  • Admin Panel: Add multiple new users at once with a CSV upload
  • Sequence Viewer
    • Perform sequence-activity relationship analyses by viewing multiple data columns in the sequence viewer
    • Select a reference sequence even when there’s no alignment
    • Retry failed alignments and cancel running alignments
  • Unification of Generic Entity and Biologics Import Pipeline: Generic Entity and Biologics import tabs are collapsed into “Biologics/Others” tab. All entities imported from “Biologics/Others” tab are virtual entities. Allows csv import of metadata on virtual entities.
  • UX Improvements
    • R-group Decomposition highlighting can now highlight the bond lines, rather than showing a halo-style highlighting around the bond
    • Users now have the quick filter options to filter by any/all types of Entities on Advanced search, just like in the Filter panel. They can choose from the options of All, Compound, R-group, Biologic and GE
    • Users can now drag a compound structure to Advanced search to create a new substructure query, like in Filter panel
    • Export data qualifiers (e.g., the greater than symbol ‘>’) as a separate column in data exports
    • Exporting a LiveReport: Choose whether to include filtered out rows, or exclude them, when exporting via the User Interface or LDClient
  • Structure Processor: A new “STRICT” processor setting allows compounds with valence violations and other invalid chemistries to be accepted or rejected by the processor. By default, compounds with valence violations are not permitted

What’s Been Fixed

  • Data and Columns Tree
    • Attempting to favorite published Limited Assay Columns in the Data & Columns Tree would fail, and now those columns can be favorited
  • Filters
    • LiveReport filters in Complex view would show additional brackets to the filter conditions, and the filter conditions would rearrange, when columns were removed from LiveReports. The arrangement of filter conditions are now properly retained and brackets do not appear
    • Removing a column from a LiveReport while viewing the Filter panel would change the Filters view from simple mode to complex mode, and now keep the view in simple mode
    • 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
  • Forms
    • Copying text from a Forms widget previously showed dotted lines in multiple widgets, and incorrectly indicated that text from multiple widgets was copied. Dotted lines now appear only around the cells within a single widget that were most recently selected
    • Forms annotation widgets would not persist styling changes (e.g., font color), and now correctly persist the changes
    • Matrix widgets included excess whitespace around values; the minimum row height has been reduced to eliminate excess whitespace
    • Viewer users could not see 3D results in the 3D Visualizer nor in Forms, and now can view 3D results
  • LiveReport Management
    • LiveReport tabs would disappear after logging out and logging in, and now correctly appear after logging back in
    • Clicking a LiveDesign hyperlink would fail to open the linked LiveReport, and instead would open the user’s last-opened LiveReport; clicking hyperlinks now correctly opens the linked LiveReport
    • The Create New LiveReport dialog now permits filtering the list of LiveReport templates
    • Attempting to apply a template to a LiveReport that contained filters would fail, and now applying a template succeeds
    • Overwriting a template would create a new template, instead of overwriting, and now correctly overwrites the template
    • Applying a template to a LiveReport would not include Limited Assay Columns contained within the template, and now correctly include Limited Assay Columns
    • Duplicating a LiveReport would not include all columns by default, and now includes all columns by default
  • Maestro Upload
    • Importing structures into Maestro from LiveDesign would occasionally fail when task status of “FINISHED” was reported before the result URL was available, and now correctly imports structures
    • Importing structures into Maestro from LiveDesign would fail if a LiveDesign model returned an empty protein file, and now the empty protein file will be skipped and not imported into Maestro
  • Model Creation
    • Configuring a Protocol in the Admin Panel to use a ${RDKIT-MOL} macro would prevent the creation of a parameterized model in the LiveDesign UI, and now correctly allows creating a parameterized model
  • Plots
    • Plot tooltips could not be dragged and moved after pinning to the screen, and now can be dragged to a new position after pinning
  • R-group Decomposition
    • Reordering R-group Decomposition scaffolds would eliminate coloring rules and sorting rules for R-group columns, and now correctly maintains coloring rules and sorting rules
  • Search
    • Advanced Search would occasionally return extra entities that didn’t match the search conditions on columns, when those columns had an undetermined data type, and now does not return extra entities
  • Sequence Viewer
    • Horizontally scrolling the sequence viewer would reset the ruler, and show the wrong residue numbers at the top, and now shows the correct residue numbers
  • User Administration
    • User email addresses would not save in LiveDesign when using Single Sign-on, and LiveReport notifications would not be emailed to users. Email addresses and are now correctly saved
    • Welcome emails for new users would include an incorrect LiveDesign URL, and now include the correct URL

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

2025 TechConnectWorld

Conference

2025 TechConnectWorld

CalendarDate & Time
  • June 9th-11th, 2025
LocationLocation
  • Austin, Texas

Schrödinger is excited to be participating in the 2025 TechConnectWorld conference taking place on June 9th – 11th in Austin, Texas. Join us for a presentation by Eric Collins, Senior Scientist II at Schrödinger, titled “Towards Complex Materials Development: Integration of Physics-Based and Machine Learning Approaches.” Additionally, Michael Rauch, Associate Director at Schrödinger will co-chair a symposium titled, “AI, Modeling, and Simulation or Advanced Materials Design.”

icon time JUN 9 | 1:30PM
Towards Complex Materials Development: Integration of Physics-Based and Machine Learning Approaches

Speaker:
Eric Collins, Senior Scientist II, Schrödinger

Abstract:
The simulation of material properties using physics-based approaches, such as density functional theory (DFT) and time-dependent DFT (TD-DFT), has proven invaluable in understanding structure-property relationships and guiding materials design. While these methods offer powerful insights, they face inherent limitations in computational scaling and cost, particularly for large-scale materials screening. Machine learning (ML) has emerged as a promising complement to traditional physics-based modeling, offering the potential to dramatically accelerate materials innovation while maintaining physical accuracy. In this talk, we first demonstrate how combining ML with physics-based approaches can overcome these challenges in designing functional materials, such as battery electrolytes, organic light-emitting diodes (OLEDs), and fluorescent dyes. By incorporating physical insights into our ML frameworks, we show how these hybrid approaches can maintain accuracy even in data-limited regimes while significantly improving computational efficiency. We then explore the extension of these methods to more complex systems, particularly formulations or mixtures of multiple materials, where emergent properties arise from subtle intermolecular interactions dependent on both structure and composition. Through the evaluation of various molecular representations and ML architectures, we demonstrate strategies for optimizing both predictive power and computational throughput. Finally, we showcase how these developed frameworks can be applied to accelerate the discovery and design of novel materials with targeted properties. This work highlights the potential of combining physics-based modeling with machine learning to advance materials innovation across multiple domains.

European Pharmaceutical Summit 2025

Summit

European Pharmaceutical Summit 2025

CalendarDate & Time
  • June 26th, 2025
LocationLocation
  • London, United Kingdom

Schrödinger is excited to be participating in the European Pharmaceutical Summit 2025 conference taking place on June 26th in London, United Kingdom. Join us for a presentation by Andrea Browning, Senior Director at Schrödinger, titled “Accelerating drug formulation through molecular dynamics simulations and machine learning approaches.”

icon time 11:50 AM
Accelerating drug formulation through molecular dynamics simulations and machine learning approaches

Speaker:
Andrea Browning, Senior Director, Schrödinger

Abstract:
Given the competitive market and inherent challenges in small molecule drug projects, selecting and combining the right ingredients, such as solvents, excipients, and polymers for drug formulation is a critical step. A smart, strategic drug formulation approach aided by a robust computational modeling platform can advance drug productization projects and inform downstream processes. Recent developments in molecular modeling techniques and machine learning methods not only enable the screening of large numbers of candidate materials in quick time, but also offer atomistic-level insights into formulation experiments and mitigate challenges in drug formulation. In this talk, I will present select examples where physics-based computational modeling tools and workflows are used to accelerate pharmaceutical formulation processes; including selection of excipients and dissolution of the final formulation product.

The NSMMS & CRASTE Symposium

Conference

The NSMMS & CRASTE Symposium

CalendarDate & Time
  • June 23rd-26th, 2025
LocationLocation
  • Norfolk, Virginia

Schrödinger is excited to be participating in The NSMMS & CRASTE Symposium taking place on June 23rd – 26th in Norfolk, Virginia. Join us for a presentation by David Nicholson, Principal Scientist I at Schrödinger, titled “Optimizing energetic binder formulations for additive manufacturing using physics-based modeling and machine learning.”

icon time
Optimizing energetic binder formulations for additive manufacturing using physics-based modeling and machine learning

Speaker:
David Nicholson, Principal Scientist I, Schrödinger

Abstract:
Plasticizers are critical components of energetic binder formulations, lending processability and flexibility to materials that would be otherwise inadequate. The identification of a good pairing between polymer and plasticizer is a formulation design problem that is challenging to solve via brute-force experimentation. Computational approaches, including ML and physics-based modeling, provide a more direct pathway to the desired material characteristics. This type of approach is especially valuable in designing materials for novel applications where identification of suitable materials is less mature and the design space is broad. In this study, we started from a design space consisting of 10 acrylate-terminated polymers and 10 energetic plasticizers and identified an optimal two-component formulation for additive manufacturing applications based on criteria for compatibility and thermomechanical properties. We utilized molecular dynamics (MD) simulations to perform an initial screening for solubility parameter differences to eliminate over half of the plasticizer-polymer pairs. For the remaining pairs, as well as the pure components, we used additional MD simulations to characterize low-temperature modulus and glass transition temperature. These simulation results were subsequently used to train formulation machine learning models for these two properties, and further utilized to identify a set of top-performing formulations using optimization. The properties of top formulations were verified using MD simulations.

AI/ML-Powered Formulation Design: Accelerating Innovation

AI/ML-Powered Formulation Design: Accelerating Innovation

Overview:

Machine learning (ML) is revolutionizing formulation design by enabling data-driven predictions of critical performance indicators, such as solubility, viscosity, stability, and even sensory properties. Chemistry-informed AI/ML models provide a powerful framework for accelerating innovation across a wide range of formulations — from personal care and food, to pharma and battery electrolytes. By analyzing large, diverse datasets, ML can predict the behavior of new formulations, including complex mixtures and ingredients that are combinations of multiple mixtures, dramatically reducing reliance on trial-and-error approaches and speeding time-to-market.

Automated solutions can integrate ingredient composition and molecular structure to generate predictive models that optimize formulation performance. This empowers R&D teams to explore complex formulation spaces, reduce development cycles, and innovate more effectively. In this webinar, we will demonstrate how Schrödinger’s integrated ML- and physics-based approaches are transforming formulation design, with an emphasis on applications relevant to consumer packaged goods (CPG).

Key Learning Objectives:

  • How physics-based models can help generate meaningful data for enhancing ML models in projects with limited data inputs
  • How an automated ML solution, incorporating chemistry and composition, can predict solubility in multi-component systems
  • How ML models that are enhanced with physics-based descriptors can improve viscosity predictions
  • How formulation ML tools enable non-computational experts to design novel CPG products that meet multiple target criteria—a case study with shampoo formulations

Who Should Attend:

  • R&D Leaders
  • Innovation Managers
  • Digitization Managers
  • Synthetic Chemists
  • Materials Scientists
  • Chemical Engineers
  • Materials Research Engineers
  • Computational Chemists
  • Computational Materials Scientists

Our Speaker

Jeffrey Sanders

Product Manager and Technical Lead for Consumer Packaged Goods, Schrödinger

Jeff Sanders received his B.S. in applied physics from Worcester Polytechnic Institute and then his Ph.D. in biophysics and molecular pharmacology from Thomas Jefferson Medical College. Since joining Schrödinger in 2013, he has served several roles. Jeff is currently the product manager and technical lead for the consumer packaged goods applications group. Additionally, he is a managing board member of the Food Engineering, Expansion, and Development (FEED) Institute, and also holds a faculty position in the Food Science Department at UMass Amherst.

Supplier’s Day 2025

Conference

Supplier’s Day 2025

CalendarDate & Time
  • June 3rd-4th, 2025
LocationLocation
  • New York, New York

Schrödinger is excited to be participating in the Supplier’s Day 2025 conference taking place on June 3rd – 4th in New York, New York. Join us for a presentation by Jeff Sanders, Research Leader at Schrödinger, titled “Multiscale Modeling for Skin Innovation: Virtual Testing of Formulations and Tyrosinase Inhibitors for Barrier Repair and Hyperpigmentation.” Stop by booth 2405 to speak with Schrödinger scientists.

icon time JUN 4 | 10:35AM
icon location 3D02
Multiscale Modeling for Skin Innovation: Virtual Testing of Formulations and Tyrosinase Inhibitors for Barrier Repair and Hyperpigmentation

Speaker:
Research Leader, Schrödinger

Abstract:
The development of next-generation cosmeceuticals—such as tyrosinase inhibitors for skin-whitening or anti-aging—requires innovation in efficacy, safety, and sustainability. To meet these demands, computational chemistry and machine learning are transforming how ingredients and formulations are designed, tested, and optimized. These tools enable virtual screening of bioactives, mechanistic insights into enzyme interactions, and predictions of formulation stability and skin permeation—all before lab work begins.
Key methods include molecular docking, molecular dynamics, and free energy perturbation (FEP+), which together help identify and rank potent inhibitors targeting tyrosinase’s active site with high precision. In parallel, machine learning accelerates formulation development by predicting critical properties such as solubility, viscosity, and shelf-life performance. Simulations also support the evaluation of interactions between formulations and packaging materials, helping to anticipate product stability over time.
Together, these approaches reduce trial-and-error in R&D, enabling faster, data-driven decisions and the creation of safer, more effective, and more sustainable cosmeceutical products.

MS Surface

MS Surface

Solution for heterogeneous catalysis and materials processing

MS Surface

Overview

MS Surface provides diverse capabilities for exploring gas-surface reactions, by finding the structure of adsorbed fragments and quantifying adsorption or desorption free energies at the quantum mechanical level.

Key Capabilities

Explore the richness of surface chemistry by enumerating structural models of surface intermediates consisting of molecules or dissociated fragments adsorbed on various surface sites
Efficiently combine multiple molecules with multiple substrates in batch mode
Compute the free energy of adsorption of the reactant gas at a specified temperature and pressure, including reactive adsorption into fragments on the surface
Compute the free energy for desorbing product molecules under specified conditions
Calculate free energies based on quantum mechanical methods, incorporating the dominant contribution to entropy from the gas-phase species
Use MS Surface results as inputs for computing reaction kinetics, ranging from the activation energy along a particular pathway to the microkinetics of the entire process

Broad applications across materials science research areas

Documentation & Tutorials

Atomic Layer Deposition

Modeling Surfaces

Related Products

MS Microkinetics

Efficient tool for surface reaction kinetics

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

MS Reactivity

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

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.

BIO 2025

Conference

BIO 2025

CalendarDate & Time
  • June 16th-19th, 2025
LocationLocation
  • Boston, Massachusetts

Schrödinger is excited to be participating in the BIO 2025 conference taking place on June 16th – 19th in Boston, Massachusetts. Join us for a panel discussion with Jenny Chambers, Senior Director of Education at Schrödinger, titled “We Still Need the People: AI/ML Drug Discovery is Here to Stay, but we Could be its Rate Limiting Factor.”

icon time JUN 18 | 2:30PM
We Still Need the People: AI/ML Drug Discovery is Here to Stay, but we Could be its Rate Limiting Factor

Panel Participant:
Jenny Chambers, Senior Director of Education, Schrödinger

Abstract:
Computational methods including AI/machine learning have the potential to be transformational in biopharma by accelerating and enhancing many aspects of drug discovery to bring better drug candidates with a higher likelihood of success to the clinic. Robust data sets are often cited as the limiting factor for this technology. However, less discussed but crucial to the success of computational drug discovery is fostering a new generation of drug hunters with multi-disciplinary training needed to make the best use of these advancements. There may be a shortage of computational chemists and molecular modelers needed to explore the vast array of opportunities that can benefit from computational drug discovery. Hear from a panel of academic and industry leaders that are developing this next-generation, what is most important for them and what the broader ecosystem can do to help fill in the pipeline gaps and ensure we have the people in place to match the technology. This session will focus on the benefits of computational methods, including AI/machine learning, to advance drug discovery, as well as the importance of fostering the next generation of scientists leveraging these vast datasets.