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Release 2026-1

Library Background

Release Notes

Release 2026-1

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Redesigned Surface Manager – Control complex visualizations effortlessly with a modern, persistent interface that allows for real-time, non-modal editing of surface styles, colors, and transparency
  • Persistent measurements – Geometric measurements now persist with the entries, allowing for uninterrupted structural comparison across multiple conformers and states
  • Standalone density map import – Accelerate Cryo-EM and crystallography workflows with direct, standalone density map import
  • Maestro Assistant modes (open beta) – A context-aware AI partner that intelligently toggles between ‘Ask’, ‘Execute’, and ‘Auto’ modes to seamlessly bridge documentation and direct action

Binding Site & Structure Analysis

Mixed Solvent MD (MxMD)

  • Added support to command line MxMD driver to seamlessly execute all simulations and compile results from combined MxMD/SiteMap cryptic pocket identification workflow

WaterMap

  • WaterMap now supports use of the OPLS_2005 forcefield and TIP4P water model

Hit Identification & Virtual Screening

Docking

  • Understand, optimize, and troubleshoot native redocking experiments with new Docking Report to maximize docking performance

Lead Optimization

FEP+

  • GraphDB/Web services can optionally download only the primary FMP file and not the FMPdb reducing time to analyzing results
  • FEP+ workflows now support execution on cost-effective preemptible nodes
  • Use 2D Sketcher to define Core SMARTS for FEP+
  • Improved handling of categorical assay data in FEP+ statistical analysis

FEP+ Protocol Builder

  • Sample three levels of salt concentrations in protocol optimization
  • Sample automatic membrane placement in protocol optimization

FEP+ Pose Builder

  • Automatically create accurate and clash-aware FEP-ready poses with FEP+ Pose Builder: Generate high-quality ligand alignments faster to run FEP+ at scale with an automated workflow designed for unbiased selection and robust atom-mapping
  • LiveDesign FEP+ Pose Builder protocol now supports generating FMP files for cycle-closure FEP calculations

Quantum Mechanics

  • Employ xTB and MLFFs including QRNN, MPNICE, and UMA in AutoTS from command line for shorter calculation times
  • Faster batch calculations by optimized CPU core assignments for all multithreaded Jaguar batch calculations

Spectroscopy

  • New corrections for C-Br and C-I bonds in 13C NMR spectra

De Novo Design

AutoDesigner – R-group Design

  • Explore large chemical spaces to identify optimal R-groups with new AutoDesigner R-group Design Panel now accessible by setting a feature flag
  • Added optional QED score (Quantitative Estimate of Drug-Likeness) for AutoDesigner R-group Design ideas

AutoDesigner – Core Design

  • Explore large chemical spaces to identify optimal core replacements with new AutoDesigner Core Design Panel now accessible by setting a feature flag
  • Added optional QED score (Quantitative Estimate of Drug-Likeness) for AutoDesigner Core Design ideas

Education Content

Biologics Drug Discovery

  • Release of MacroMolecular Pose Filter – Select the most plausible or relevant structural models of a macromolecular complex from a larger set of generated possibilities
  • New protein descriptor – ASPmax – Added ASPmax (Maximum Average Surface Property) to our descriptor set. Used to predict the retention time of proteins in hydrophobic interaction chromatography (HIC) columns and aggregation risk
  • Search and filter non-standard residues – Support for text-based searching and filtering of non-standard residues based on labels in the Name, Code, and Description columns
  • Residue Lookup in the MMGBSA residue scanning panel – Quickly search and find residues to mutate
  • Classification of residue scanning results – Color codes residue scanning results to designate positive, neutral or negative mutational variants based on energy score cut-offs

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Defect Formation Energy: Workflow solution to analyze point defects in crystals
  • Options to set frequency cutoff / harmonic threshold in the Phonon DOS Viewer
  • Support for TB09 density functional (command line)
  • Support for rVV10-SCAN density functional (command line)
  • Support for NpT ensemble in QE BOMD simulations (command line)
  • (+MATSCI_NEB_MLFF) MLFF integration in NEB

MS Surface

Product: MS SurfChem

  • Desorption Enumeration: WAM to open results in Adsorption Energy

Microkinetics

Product: MS Microkinetics

  • Option to view selectivities and degrees of selectivity control
  • Option to load/save archived MKM output
  • Option to export reaction view as an image (PNG) file
  • Results from individual stages made visible from the analysis panel

Optoelectronics Genetic Optimization

Product: Genetic Optimization (GA)

  • Support for setting target property based on models from ML Property Prediction

Active Learning Optoelectronics

Product: Active Learning Optoelectronics

  • Option to set target values excluded from optimizations

Reactivity

Product: MS Reactivity

  • Nanoreactor: Option to specify separate hosts for driver and subjobs
  • Nanoreactor: (+NANOREACTOR_AUTOTS) Automatic transition state search for elementary reaction network calculations via AutoTS
  • Nanoreactor: Option to skip generating trajectory files
  • Nanoreactor: User control over the time interval between trajectory frames
  • Reaction Network Profiler: Option to refine conformer geometries using UMA (MLFF)
  • Reaction Network Profiler: Option to assign stoichiometric multipliers for reactants

Transport Calculations via MD simulations

Product: MS Transport

  • Ionic Conductivity: Support for MLFF

Dielectric properties

Product: MS Dielectric

  • Complex Permittivity: Support for multi-component systems

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • CG FF Builder: Improved detection and mapping of non-isomorphic residues
  • CG FF Builder: Automated particle naming scheme with chemical context
  • CG FF Assignment: Up to 15x speed-up for models with a large number of particle types
  • Coarse-Grained Mapping: Residue number and name retained through mapping
  • Coarse-Grained Mapping: Option to import SMARTS patterns from previous use
  • Speed-up for DPD simulations of up to 30% with improved cutoff margins

Complex Bilayer Builder

Product: MS Complex Bilayer Builder

  • Complex Bilayer: (+COMPLEX_BILAYER_BUILDER_EXTENDED_LIPID_LIB) Expanded list of default lipids
  • Complex Bilayer: Increased limit for water padding depth to 5000 Å
  • Complex Bilayer: Support for custom-trained OPLS
  • Membrane Analysis: (+MEMBRANE_ANALYSIS_PREP_FOR_FEP) Support for generating poseviewer formatted files compatible with FEP calculations
  • Membrane Analysis: Support for applying multiple leaflet-finding algorithms

Materials Informatics

Product: MS Informatics

  • Machine Learning Property: Improved panel interface for model selection
  • MD Descriptors: User control over simulation system size (max # of atoms)
  • MD Descriptors: Support for formulation input with path assigned to structure files
  • MLFF Calculations: Option to set constraints to atomic positions
  • MLFF Calculations: Support for running on GPU nodes

Formulation ML

Product: MS Formulation ML

  • Formulation ML: ‘Learned Fingerprint’ as a new option to feature space
  • Formulation ML: Advanced option to process (‘impute’) training set data with partially missing descriptors
  • Formulation ML: Improved UI for parity plot
  • Formulation ML: Support for building machine learning models using training datasets with missing chemical (SMILES) information
  • Formulation ML: Target property displayed in the ‘Performance’ tab
  • Formulation ML: User control over correlation threshold between features
  • Formulation ML Optimization: Support for custom-ingredient descriptors
  • Formulation ML Optimization: Support for multi-CPU parallelization
  • Formulation ML Optimization: Option to use genetic algorithms for formulation optimization

Layered Device ML

Product: MS Layered Device ML

  • OLED Device ML: User control over correlation threshold between features
  • OLED Device ML: Target property displayed in the ‘Performance’ tab

MS Maestro Builders and Tools

  • Adsorption Enumeration: WAM to open results in Adsorption Site Finder / Adsorption Energy
  • Adsorption Site Finder: WAM to open results in Adsorption Energy
  • Adsorption Enumeration: Improved organization of output structures in the Project Table
  • Adsorption Site Finder: Up to 200x of speed-up for jobs using MLFF
  • Clean Up Structures: Support for MLFF
  • Disordered System: Option to define residue name for components
  • Support for converting *.vis files to *.cub formatted files (command line)
  • Polymer: Import of coupling probabilities from a CSV formatted file
  • Polysaccharide: (+POLYSACCHARIDE_BUILDER) Simplified model building solution for linear-chain polysaccharides
  • Single Complex: Updated list of bridging ligands
  • Query Bonds: Display of polyhedra for molecular crystals
  • Query Bonds: Search for and modification of non-bonded atom pairs

Classical Mechanics

  • Droplet: Support for using pre-assembled droplet models
  • Droplet: Support for computing contact angles with hydrate surfaces
  • Elastic Constants: Support for MLFF
  • (+ALLOW_OLD_FORCEFIELD_PARAMETERS) Support for running MD using OPLS4/OPLS5 parameters with backwards compatibility (2025-4 and older)
  • MD Multistage: FF type for the input structure displayed in the panel
  • Stress Strain: Support for MLFF
  • Stress Strain: Option to use velocities from previous strain steps
  • Surface Tension: Improved analysis with block averaging scheme
  • Tg: (+THERMOPHYSICAL_PROPERTIES_MLFF) Support for MLFF
  • Umbrella Sampling: Workflow solution to run umbrella sampling algorithm for small molecules near lipid and surfactant bilayers
  • Umbrella Sampling: Displaying quantity of overlap between windows
  • Viscosity: Adjusted default timestep (0.5 fs) for MLFF simulations

Quantum Mechanics

  • Adsorption Energy: Option to pre-optimize structures with MLFF
  • Adsorption Energy: Support for atomic positional constraints with MLFF
  • Crest: (+MATSCI_CREST_QCG) CREST Quantum Cluster Growth Utility
  • QM Multistage: Option to select GFN2-xTB from the list of theory
  • Optoelectronic Film Properties: Display of refractive index ratio per molecular species
  • Reaction Network Viewer: Comprehensive analysis viewer GUI for viewing networks created by Reaction Network Profiler and Nanoreactor

Education Content

Education Content

Life Science

Materials Science

LiveDesign

What’s New in 2026-1

  • Biologics
    • Design new biologics with point mutations using natural monomers
    • Upload custom monomers via API access, and view the monomers in the sequence viewer
    • Search for a subsequence within an annotated region of a Biologics entity, by selecting the annotation and numbering scheme from pre-filled dropdowns
    • Users can now color residues by property in the sequence viewer with any model that outputs the per-residue property/properties and color scheme mapping(optional) in the specified format, or with any Freeform column that contains the output
    • The “Biologics” and “Generic Entity” options in the “Type” dropdown menu of the Advanced search panel, have been unified to “Biologics/Others”
    • View branched and cyclic peptides in the sequence viewer
  • LiveDesign AI Assistant: interact with LiveDesign using an AI assistant to create Freeform and Formula columns, create and update coloring rules, perform data analyses and plot data within the Assistant, and instantly find help documentation. Note that this capability requires the LiveDesign ML plugin
  • Project Dashboards
    • View a project activity stream of newly added assay data and comments using the new “Activity” section
    • Entity count statistics and recently added entities shown in the Project Dashboard will now include all entities that are searchable to the project (e.g., compounds imported to unrestricted projects), instead of entities specifically imported to the project
  • Models: Column as parameter models that use a 3D column as input now have access to the favorited pose, and the pose order that is represented in the LiveReport
  • UX Improvements
    • Filter out un-run, failed, and pending model cells from your LiveReport
    • Drag a compound structure from the main spreadsheet directly to the Design, Search,or Advanced search panels without opening the panel first
    • Close LiveReport tabs by clicking on them with a middle mouse button click
    • 3D Visualizer: apply Stereolabels, Element label, and Atom Number labels to the selected atoms/residues/Chains

What’s Been Fixed

  • Formulas that used the Lot Registration Date column as input would fail to calculate, and cause a red error bar to appear on the LiveReport. Those formulas now calculate correctly and do not cause an error
  • Icons within the main spreadsheet cells to view a pose in the 3D visualizer, or in Maestro, would disappear when the cell was resized, and now remain visible
  • Adding a click-to-run model column to a matrix widget would cause the widget to crash and not show data, and now click-to-run models appear correctly in the matrix widget
  • Formulas using if() statements would fail to calculate when the if() statement used experimental assay cells that contained multiple values, in which one of those values was Null. Those formulas now calculate correctly
  • The “Creating New Layout” dialog did not show an option to copy an existing layout, and now correctly shows that option.
  • Editing a LiveReport’s title using the “Edit LiveReport Dialog” would result in moving the LiveReport to the Project Home folder, and now editing a LiveReport with that dialog will not move the LiveReport to a different folder
  • Formatting option buttons on the Configure Matrix Widget dialog overlapped, which prevented accessing some formatting options, and now the buttons do not overlap
  • Experimental assay data would show dashed lines underneath experimental values within the spreadsheet cells, and now do not show dashed lines
  • The aggregateMax() and aggregateMin() formula functions now work for date columns
  • Multi-chain or branched peptides are now accurately identified with incremental peptide numbers in their identifiers in sequence viewer.
  • The sequence viewer now shows a message when TCRs with unsupported numbering schemes are used, and suggests to move to a supported numbering scheme for TCRs in the sequence viewer.
  • When 3D visualizer is drilling down from the sequence viewer, selecting another entity in the sequence viewer does not reset the existing residue selection in the sequence viewer and 3D visualizer
  • Parameterized models that used another model’s image columns as input would not calculate results, and now correctly calculate
  • Recalculating a deleted model return would result in a permanently flashing cell, and now the cell will correctly show a Failed message
  • When models returned a 3D output of type “other”, that 3D output was not accessible to other parameterized models, and now is accessible
  • LDClient: the method get_models_by_name now includes an option to ignore archived models

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

LiveDesign ML

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LiveDesign ML is your complete, centralized solution for deploying and maintaining advanced machine learning models to accelerate and guide drug discovery programs. Acting as your ML co-pilot, it democratizes AI/ML model generation with a fully automated, high-throughput workflow and provides a seamless and effortless way to build, validate, and deploy models for critical tasks. By integrating directly into your central design platform, LiveDesign ML ensures your team always has access to the most accurate, scalable predictions without the burden of complex model deployment or maintenance.

 

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  • Comprehensive property profiling Access advanced capabilities to rapidly profile, filter, and prioritize compounds across all discovery programs.
  • Integrated synthetic accessibility Streamline your design-make-test cycle with Retrosynth predictions to ensure molecules are synthetically viable before they are made.

Set it and forget it, ML models made accessible and comprehensive for all

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ML-powered engine that helps you prioritize the most promising leads by training custom machine learning models on your chemical data to accurately forecast the physical and chemical profiles of novel molecular structures.

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TuneLabTM is a collaborative platform created to offer access to AI/ML tools leveraging Lilly’s own drug discovery models.

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Benchmark study of DeepAutoQSAR, ChemProp, and DeepPurpose on the ADMET subset of the Therapeutic Data Commons

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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.

Learn More

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.

Learn More
Other Resources

Diverse computational strategies enable the discovery of p38α-MK2 molecular glues

Posted on January 9, 2026February 5, 2026 by Anonymous

FEB 5, 2026

Diverse computational strategies enable the discovery of p38α-MK2 molecular glues

Molecular glues continue to offer drug hunters novel opportunities to target “undruggable” proteins – given their ability to enhance protein-protein interactions, their small size, and advantageous physicochemical properties (as compared to PROTACs). Recent work done by Schrödinger’s therapeutics group has shown how p38α-MK2 molecular glues can be designed that demonstrate superior properties relative to traditional orthosteric inhibitors. The resulting compounds have already demonstrated impact, as shown by a pronounced reduction in TNFα levels after PO dosing in LPS mouse models, and represent the validation of this modeling workflow for molecular glues.

In this webinar, Schrödinger’s medicinal and computational chemists will show how they used a multipronged computational design strategy to discover multiple structurally diverse, potent, and highly selective molecular glues. By using Schrödinger’s industry-leading free energy perturbation technology (FEP+), coupled with AutoDesigner, and machine learning tools (including AL-FEP and generative ML), the team successfully navigated vast chemical space while optimizing across multiple project criteria. For R&D teams, this workflow provides a blueprint for tackling challenging targets and accelerating the discovery of novel molecular glues for your own complex protein systems.

Webinar Highlights:

  • Learn new in silico strategies for the discovery of structurally diverse, potent, and selective molecular glues
  • See how Schrödinger’s medicinal and computational chemists use enumerations, physics-based methods, and AI/ML tools to tackle drug discovery and multiparameter optimization challenges
  • Ask questions to gain further insight from the speakers to apply to your work

Our Speakers

Hideyuki Igawa

Senior Director, Schrödinger

Hideyuki Igawa is a senior director in the therapeutics group at Schrödinger, where has been leading multiple drug discovery programs using Schrödinger’s computational platform. He received his MS in Chemistry from Kyoto University, then obtained his Ph.D. in Pharmaceutical Sciences from Nagoya City University. He previously worked at Takeda Pharmaceuticals and Tri-Institutional Therapeutics Discovery Institute, where he contributed to the discovery of multiple small molecule drug candidates towards the clinic.

Markus Dahlgren

Senior Principal Scientist, Schrödinger

Markus Dahlgren is a computational chemist at Schrödinger, where he has led drug discovery efforts using molecular modeling technologies since 2013. He received his Ph.D. in Organic Chemistry from Umeå University in Sweden in the laboratory of Professor Mikael Elofsson and subsequently completed a postdoctoral fellowship at Yale University in the laboratory of Professor William Jorgensen. His expertise bridges synthetic organic chemistry and computational methods, accelerating the discovery and development of novel small-molecule therapeutics.

InventU Sustainable Future Congress 2026

Posted on December 11, 2025January 20, 2026 by Anonymous
Conference

InventU Sustainable Future Congress

CalendarDate & Time
  • February 25th-26th, 2026
LocationLocation
  • Amsterdam, Netherlands
Register

Schrödinger is excited to be participating in the InventU Sustainable Future Congress conference taking place on February 25th – 26th in Amsterdam, Netherlands. Join us for a presentation by Jeff Sanders, Research Leader at Schrödinger, titled “From Natural Ingredients to Packaging: Computational Strategies for Sustainable Personal Care Products.” Stop by booth S14 to speak with Schrödinger scientists.

icon time FEB 25 | 16:10
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From Natural Ingredients to Packaging: Computational Strategies for Sustainable Personal Care Products

Speaker:
Jeff Sanders, Research Leader, Schrödinger

Abstract:
Research and development in cosmetic and personal care products increasingly face sustainability-driven challenges, including reducing development time and resource consumption while limiting reliance on new or scarce raw materials. To address these constraints, predictive modeling, formulation machine learning, and natural product ingredient characterization provide complementary approaches to accelerate sustainable innovation. Formulation machine-learning models leverage existing experimental and performance data to optimize ingredient combinations, reduce redundant testing, and guide reformulation toward more sustainable and bio-based alternatives. Computational chemistry methods enable molecular-level characterization of natural ingredients, improving understanding of their structural diversity, physicochemical properties, and stability within complex formulations. These tools also provide insight into formulation morphology, interactions with biological surfaces such as skin and hair, and product–packaging interactions that influence shelf-life and material compatibility. Through representative case studies, we demonstrate how integrating formulation ML with physics-based simulations reduces trial-and-error experimentation, maximizes the value of existing data, and supports the design of high-performance, sustainable cosmetic products from formulation through packaging and end use.

Register

SFCi 2025

Posted on November 21, 2025December 1, 2025 by Anonymous
Conference

SFCi 2025

CalendarDate & Time
  • December 10th-11th, 2025
LocationLocation
  • Paris, France
Register

Schrödinger is excited to be participating in the 12th conference of the Société Française de Chémoinformatique (SFCi) taking place on December 10th – 11th in Paris, France. Join us for a presentation by David Papin, Principal Scientist II, Applications Science at Schrödinger, titled “Modern Virtual Screening workflows.”

icon time DEC 10 | 18:00 – 18:15
Modern Virtual Screening workflows

Speaker:
David Papin, Principal Scientist II, Applications Science at Schrödinger

Abstract:
Schrödinger has a long history of developing virtual screening technologies. Modern virtual screening faces new challenges, particularly with the emergence of ultra-large chemical libraries over the past 10 years. As Schoichet et al. highlighted [1], there is a clear need to explore a much larger chemical space to improve the number and quality of hits found. We will be presenting a modern virtual screening workflow that efficiently screens ultralarge libraries. This workflow combines ligand-based approaches and machine learning-guided docking with advanced scoring methods, such as – 1D-sim which measures molecular similarity by projecting 2D structures into a single atomic coordinate [2]. When combined with Shape Screening, it gives rise to a cascaded screening workflow named QuickShape [3]. – GlideWS [4]: an advanced docking method that combines enhanced ligand sampling and a physics-based empirical scoring function to improve hit discovery and pose prediction in virtual screening – ABFEP (Absolute Binding Free Energy Perturbation) [5]: a highly accurate, physics-based computational method that calculates absolute binding free energy We will also emphasize the benefits of screening large libraries with a combination of machine learning and physics-based methods (Active Learning workflows). 

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Scaling FEP+ for success: Strategic deployment of FEP+ and AI/ML to accelerate chemical space exploration

Posted on November 18, 2025December 10, 2025 by Anonymous

DEC 10, 2025

Scaling FEP+ for success: Strategic deployment of FEP+ and AI/ML to accelerate chemical space exploration

The ultimate challenge in modern drug discovery is converting scientific rigor into organizational scale and speed. While FEP+ provides the gold standard in predictive power, its full potential is unrealized when deployment is siloed. To access untapped potential and eliminate wasted resources, you must first address the bottlenecks and fragmentation across the project that are hindering the shift to a truly “predict-first” enterprise.

In this session, we will share experiences from expert users detailing the different tiers of FEP+ implementation and the necessary architectural support at each stage to demonstrate success. We will show how proper deployment, particularly through integration with AI/ML workflows, fundamentally changes the pace of exploration, enabling full chemical space mapping and in silico multiparameter optimization (MPO). This strategy empowers the entire project team, democratizing predictive insight and eliminating bottlenecks to design better drugs, faster.

Join us to map out your strategy for maximizing the organizational impact of FEP+ and to achieve the full potential of your computational drug discovery and business goals.

Webinar Highlights

  •  Introduction to the different levels of FEP+ deployment, guiding implementation from initial use to full enterprise integration
  •  Discussion of how integrating FEP+ with AI/ML workflows drives exponential acceleration in chemical space exploration and optimization
  •  Demonstration of how scaling FEP+ eliminates bottlenecks and empowers entire project teams to accelerating DMTA cycles as shown by Schrödinger’s therapeutics group success stories

Our Speakers

Aditya Kaushik

Senior Scientist II, Life Science Software, Schrödinger

Aditya Kaushik is an ML Research Scientist and the lead developer for the Generative Design and Retrosynthesis technologies at Schrödinger. His primary focus is on the research, development and integration of machine learning approaches to accelerate and optimize Design-Make-Test-Analyze (DMTA) cycles in active drug discovery programs. He received his B.S. from Johns Hopkins University, where he double majored in Computer Science and Chemical & Biomolecular Engineering.

Pieter Bos

Principal Scientist II, Schrödinger

Pieter Bos, Ph.D., is a principal scientist and product manager of AutoDesigner and De Novo Design workflows. At Schrödinger, his main focus is the research, development and optimization of automated compound design algorithms. Lead scientist for the design and execution of enumerated drug molecule libraries for internal and collaborative drug design projects. He received his Ph.D. in Synthetic Organic Chemistry from the University of Groningen in the laboratory of Prof. Ben Feringa. Prior to joining Schrödinger, he worked as a postdoctoral researcher in synthetic methodology development at Boston University (Prof. John Porco and Prof. Corey Stephenson) and small molecule drug discovery at Columbia University (Prof. Brent Stockwell).

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Starter Platform: Implementing Digital Drug Discovery

Empower digital discovery across entire project teams

Maestro+LiveDesign offers industry-leading computational modeling tools in a flexible, cloud-native working environment for your entire discovery team — spanning both small and large molecule research. Streamline workflows with centralized access to all project data (experiment and in silico predictions), cutting-edge computational modeling tools, and collaborative decision-making technology — all in a single interface.

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This starter platform package includes:

Maestro is Schrödinger’s streamlined portal to access state-of-the-art predictive computational modeling and machine learning workflows for molecular discovery

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LiveDesign is the digital platform for modern drug discovery teams – powering collaboration anytime, anywhere

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  • Check greenCombine the powers of predictive modeling and real-time data management to drive fewer, faster design cycles
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Work with our team of solutions architects to customize your deployment

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Schrödinger provides expert support, educational materials, and training resources designed for both novice and experienced users

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  • Check greenLevel up your skillset with hands-on, online molecular modeling training courses available on-demand
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ChemAI 2025

Posted on October 21, 2025October 21, 2025 by Anonymous
Conference

ChemAI 2025

CalendarDate & Time
  • November 21st, 2025
LocationLocation
  • Amsterdam, Netherlands
Register

Schrödinger is excited to be participating in the ChemAI 2025 conference taking place on November 21st in Amsterdam, Netherlands. Join us for a presentation by Anand Chandrasekaran, Senior Principal Scientist at Schrödinger, titled “Revolutionizing Materials R&D with Combined Physics-Based and Machine-Learning Approaches.”

icon time 11:25 – 11:40
Revolutionizing Materials R&D with Combined Physics-Based and Machine-Learning Approaches

Speaker:
Anand Chandrasekaran, Senior Principal Scientist at Schrödinger

Abstract:
Advances in materials discovery increasingly rely on merging the predictive power of physics-based simulation with the speed and adaptability of machine learning. At Schrödinger, we integrate molecular dynamics (MD), quantum mechanics, and data-driven models to accelerate property prediction and design.

In collaboration with SABIC, machine-learning models augmented with MD simulations accurately predicted polymer glass-transition temperatures, dielectric constants, and refractive indices, guiding the selection of next-generation polycarbonates. With Panasonic, large-scale MD and reinforcement learning were combined to design and experimentally validate ultra–low-viscosity solvents for advanced electrolytes.

Extending these principles, our Formulation ML solution predicts the properties of complex mixtures by linking molecular structure, composition, and simulation-derived descriptors to target physical properties. Together, these efforts show how integrating physics-based insight with machine learning accelerates innovation, improves interpretability, and delivers experimentally validated materials far faster than traditional R&D.

Accelerating materials discovery with physics-informed AI/ML

Posted on September 19, 2025November 6, 2025 by Anonymous

Accelerating materials discovery with physics-informed AI/ML

Speaker:

Saientan Bag, Senior Scientist I, Schrödinger

Abstract:

Artificial Intelligence (AI) and machine learning (ML) are reshaping materials science, accelerating the discovery of novel materials and optimizing formulations with unprecedented speed and precision. From polymers to catalysts, these tools unlock design possibilities once thought unattainable. But can AI/ML succeed without a foundation in physics and chemistry? Can we overlook decades of scientific understanding in favor of purely data-driven approaches? At Schrödinger, we combine physics-based simulations with ML built on chemically meaningful representations. This synergy improves accuracy, reduces experimental costs, and delivers insights even in data-limited scenarios. In this webinar, we will explore how Schrödinger’s AI/ML approach is transforming materials R&D through real-world case studies. Our innovation operates on two levels: first, by improving the accuracy-efficiency trade-off in atomistic simulations through the development of machine learning force fields (MLFFs) for high-throughput, accurate modeling; and second, by directly applying AI/ML techniques to predict and optimize material properties in applications such as consumer goods, battery electrolytes, polymers, and catalysts.

Lunch & Learn: Informatics for Medicinal Chemists

Posted on September 18, 2025October 8, 2025 by Anonymous
Lunch and Learn
CalendarDate & Time
  • October 13th, 2025
  • 10:30 – 15:30 BST
LocationLocation
  • Cambridge, United Kingdom

Informatics for Medicinal Chemists

Register

Dear Medicinal Chemists,

Ever feel your DMTA cycles are not advancing as quickly as you’d like? Is it a challenge to bring all your data from in silico predictions to experimental results into one central view to quickly decide what to design next? Effectively sharing hypotheses with your team and securely tracking data with CRO partners present their own sets of challenges.

Schrödinger therefore invites you to a specialized and free-of-charge “Lunch & Learn” workshop on Monday, October 13th at the Clayton Hotel Cambridge, designed to tackle these exact workflows and collaboration challenges.

We’ll be diving deep into our informatics platform, LiveDesign, to show you how all members of a drug design team can work together to solve these challenges. At this event, Schrödinger will host a hands-on CDK2 inhibitor design challenge where you’ll be able to use LiveDesign.

Date & Time: 

Monday, October 13, 2025
From 10:30 to 15:30 BST

Program: 

Part 1: Welcome Coffee and Introductory Talk about the Platform and Success Stories

10:30 – 12:00 BST

Olivia Lynes, Senior Strategic Deployment Manager II, Enterprise Informatics

+ Lunch
12:00 – 13:00 BST

Part 2: Workshop and Design Challenge on CDK2 Inhibitor with LiveDesign

13:00 – 14:30 BST

Olivia Lynes, Senior Strategic Deployment Manager II, Enterprise Informatics

Hands-on CDK2 inhibitor design challenge where you’ll be able to use LiveDesign on:

  • Predicted physicochemical properties.
  • Machine learning models.
  • Ligand Designer: a validated docking and design model.
  • A tool which searches ChEMBL and vendor databases (with over 1 billion total compounds) at rapid speeds to estimate the novelty of designed compounds.
  • A Target Product Profile MPO.

Part 3: Interactive Q&A and Networking Session

14:30 – 15:30 BST

The afternoon session will feature a Q&A and networking session, providing an opportunity to present your questions and challenges, which the Schrödinger team will endeavor to address.

You can either join for the whole event or solely for the presentation session. All you need to bring is a laptop – no software installation is required. During the workshop, lunch will be served. The afternoon will feature an interactive Q&A and networking session, providing an opportunity to present your questions and challenges, which the Schrödinger team will endeavor to address.

We look forward to seeing you in Cambridge!

Register today to secure your seat!

The workshop is free to attend but preregistration is required as seats are limited. Previous-experience with the Schrödinger suite is not required.

Register

248th ECS Meeting

Posted on September 12, 2025September 12, 2025 by Anonymous
Conference

248th ECS Meeting

CalendarDate & Time
  • October 12th-16th, 2025
LocationLocation
  • Chicago, Illinois
Register

Schrödinger is excited to be participating in the 248th ECS Meeting taking place on October 12th – 16th in Chicago, Illinois. Join us for presentations by Schrödinger scientists.

icon time OCT 14 | 11:00AM
Schrödinger’s Atomistic Simulation Workflow to Model Solid Electrolyte Interphase in Lithium-Ion Batteries

Speaker:
Manav Bhati, Senior Scientist II, Materials Science Modeling Services, Schrödinger

Abstract:
Lithium-ion batteries (LiBs) are ubiquitous, powering applications from portable electronics to electric vehicles. Atomic-level computational simulations play a critical role in exploring and optimizing battery materials. This work expands the application of our physics-based simulation workflow to model the solid electrolyte interphase (SEI), which is a crucial yet poorly understood component of batteries. Our approach utilizes a reaction-template-based method with the OPLS4 force field and a high-speed GPU-based molecular dynamics engine (Desmond) within Schrödinger’s Materials Science suite to simulate SEI nucleation and growth. The SEI simulator provides detailed atomistic insights into SEI morphology and product distribution. In particular, we investigate how changing the chemistry of electrolytes affects SEI composition and properties.   Lithium hexafluorophosphate in ethylene carbonate (LiPF6/EC) is a widely used Li-ion battery electrolyte. Our atomistic simulations of 1 M LiPF6/EC on a graphite electrode closely match experiments, revealing a thin inorganic layer (Li2CO3, LiF) near the electrode, a porous organic layer (Li2EDC, Li2BDC), and gaseous species (C2H4, PF3) diffusing away. Comparing different electrolyte chemistries, we find that 1 M LiPF6/EC forms a denser, more compact SEI than 1 M LiPF6/PC, suggesting superior mechanical stability and explaining EC’s dominance in commercial batteries. Adding ethyl methyl carbonate (EMC, a common linear cosolvent) to EC further enhances SEI density, particularly in the inorganic layer, leading to reduced electrolyte degradation, lower irreversible losses, and improved mechanical stability, ultimately boosting battery performance.   Schrödinger’s SEI simulation workflow enables modeling across diverse electrolyte chemistries (from cyclic to linear electrolyte solvents and mixtures), offering comprehensive atomistic insights that accelerate the development of optimized materials for next-generation batteries.

icon time OCT 15 | 12:20PM
Scalable and Generalizable Machine Learning Force Fields for Modeling Complex Battery Materials

Speaker:
Garvit Agarwal, Principal Scientist I, Materials Science Applications Science

Abstract:
The rapid advancements in rechargeable Li-ion battery (LIB) technology has revolutionized several key industries such as automotive and consumer electronics. However, new battery chemistries are needed to improve the power density, safety, reliability, and lifetime of LIBs. Existing classical force fields are not accurate enough to predict bulk properties of LIB materials without time-consuming and customized parametrization. To move towards accurate and reliable modeling of battery chemistries, we developed a machine-learned force field (MLFF) using a charge recursive neural network (QRNN) architecture, which includes both long-range interactions and global charge redistribution. The MLFF is trained to model a large chemical space of industrially relevant liquid electrolyte chemistries and enables large scale molecular dynamics (MD) simulations of realistic electrolyte formulations. In this presentation, I will demonstrate our generalized active learning framework and sampling workflow used to generate accurate training data for liquid and amorphous systems. I will discuss large scale benchmarks carried out to evaluate the performance of MLFF against experimental data for key physical and transport properties of liquid electrolytes including density, viscosity and ionic conductivity. Our results indicate that MLFF outperforms the classical force fields in terms of quantitative agreement with experimental data across a broad range of electrolyte chemistries. I will also discuss the novel molecular level insights into the unique Li+ cation solvation structures predicted by MLFF and their validation using experimental nuclear magnetic resonance (NMR) spectroscopy. Finally, I will briefly discuss our recent work to develop a message passing neural network architecture to train universal MLFF for inorganic materials covering up to 94 elements from the periodic table allowing for efficient design of materials for cathodes, coatings and and solid-state electrolytes for applications in next-generation batteries. 

SEPAWA CONGRESS 2025

Posted on September 3, 2025September 30, 2025 by Anonymous
Conference

SEPAWA CONGRESS 2025

CalendarDate & Time
  • October 15th-17th, 2025
LocationLocation
  • Berlin, Germany
Register

Schrödinger is excited to be participating in the SEPAWA CONGRESS 2025 conference taking place on October 15th – 17th in Berlin, Germany. Join us for a poster session and presentation by Jeff Sanders, Research Leader of Materials Science Product Discovery at Schrödinger. Stop by booth D564 & 565 to speak with Schrödinger scientists.

icon time OCT 16 | 11:15
icon location Room 12 + 13
Lecture: Molecular modeling of a hair fiber surface by coarse-grained simulation

Speaker:
Jeff Sanders, Research Leader of Materials Science Product Discovery, Schrödinger

Abstract:
Further understanding of the physical properties of the hair surface and its interactions with commonly used ingredients would help to drive new development for hair care products. Molecular simulation can provide an accurate predictive model on the outer layer of the hair to help researchers and engineers understand the fundamental physics at molecular level.1,2 In this study, we have built a MARTINI coarse-grained (CG) model focusing on the description of 18-methyl eicosanoic acid (18-MEA). The CG model was derived from an all-atom model but it overcomes the size limits of the all-atom model.3 We first used the model to characterize the hair surface to understand the distribution of 18-MEA patches. Then the model was used to virtual test the interaction of ingredients on the hair surface. Through modeling of grease molecules and shampoo surfactants on the F-layer of the hair surface, the in-situ cleaning and conditioning process are revealed at molecular scale resolution, which can be correlated to the processes of cleaning and conditioning when washing macroscopically.The MARTINI CG model provides an opportunity to understand the hair surface under different conditions. The unique mechanistic insight of these simulations can help enrich the knowledge of the functioning of the products and help optimize the product performance.

icon time OCT 16 | 14:30
icon location Hall Europa
Poster: Beyond AI: Leveraging physics-based modeling and machine learning to develop new cosmetic products

Speaker:
Jeff Sanders, Research Leader of Materials Science Product Discovery, Schrödinger

Abstract:
In today’s dynamic market, businesses are spearheading a sustainability revolution, propelling the exploration of biomaterials to the forefront. Harnessing the power of cutting-edge data-driven multi-scale physics simulations and machine learning, researchers are meeting demand with unprecedented speed and precision. Join us for a dive into how these simulations are transforming cosmetics R&D, with illustrative real-world case studies from industrial collaborations. Experience the fusion of science and sustainability, shaping a vibrant, eco-conscious future.

Register