- Publication
- Nov 19, 2024
Olefination with sulfonyl halides and esters:Mechanistic DFT and experimental studies, andcomparison with reactivity of phosphonates
Basak, et al. Elements, 2024- Publication
- Nov 19, 2024
Exploring Molecules with Low Viscosity: Using Physics-Based Simulations and De Novo Design by Applying Reinforcement Learning
Matsuzawa, et al. Chemistry of Materials, 2024, 36(23), 11706-11716- Publication
- Nov 4, 2024
Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models
Chew, et al. Machine Learning: Science and Technology, 2024, 5, 045031- Publication
- Nov 1, 2024
Designer Fluorescent Redoxmer Self-Reports Side Reactions in Nonaqueous Redox Flow Batteries
Robertson, et al. Batteries & Supercaps, 2024- Publication
- Oct 29, 2024
Photooxygenation reactions under flow conditions: An experimental and in-silico study
Moroni, et al. Organic Chemistry, 2024, Preprint- Publication
- Oct 28, 2024
Modelling of Prednisolone Drug Encapsulation in Poly Lactic-co-Glycolic Acid Polymer Carrier Using Molecular Dynamics Simulations
Acharya, et al. Journal of Pharmaceutical Innocation, 2024, 19(70)- Publication
- Oct 24, 2024
Low pKa Phosphido-Boranes Capture Carbon Dioxide with Exceptional Strength: DFT Predictions Followed by Experimental Validation
Riasati, et al. The Journal of Physical Chemistry Letters, 2024, 15(43), 10909-10917- Publication
- Oct 23, 2024
Cu-TiO2/Zeolite/PMMA Tablets for Efficient Dye Removal: A Study of Photocatalytic Water Purification
Armaković, et al. Catalysts, 2024, 14(11), 746- Publication
- Oct 18, 2024
Steviol rebaudiosides bind to four different sites of the human sweet taste receptor (T1R2/T1R3) complex explaining confusing experiments
Hao, et al. Communications Chemistry, 2024, 7(236)- Publication
- Oct 16, 2024
A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials
Abdellah, et al. New Journal of Chemistry, 2024, 44- Publication
- Oct 2, 2024
Development of Glecaprevir: Conformations, Crystal Structures, and Efficient Solid–Solid Conversion for a Highly Polymorphic Macrocyclic Drug
Chen, et al. Crystal Growth & Design, 2024, 24(2), 8270-8284- Publication
- Oct 2, 2024
Predicting the Release Mechanism of Amorphous Solid Dispersions: A Combination of Thermodynamic Modeling and In Silico Molecular Simulation
Walter, et al. Pharmaceutics, 2024, 16(10), 1292Case Studies
- Jul 11, 2025
Advancing sustainable food processing through integrated experimental and molecular simulation approaches
Scientists from Schrödinger and UMass carried out comprehensive studies experimentally and computationally to investigate the key properties and extrusion performance of zein-formulated meat alternatives.
Documentation
- Documentation
Machine Learning Force Fields
Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.
- Documentation
MS Transport
Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules.
Events
- Sep 30th – Oct 2nd, 2025
Festival of Biologics Basel 2025
Schrödinger is excited to be participating in the Festival of Biologics 2025 conference taking place on September 30th – October 2nd in Basel, Switzerland.
- Oct 1st-2nd, 2025
Simulation World Detroit
Schrödinger is excited to be participating in the Simulation World Detroit conference taking place on October 1st – 2nd in Plymouth, Michigan.
- Oct 1st-3rd, 2025
Structure-Based Drug Design Conference 2025
Schrödinger is excited to be participating in the Structure-Based Drug Design Conference 2025 taking place on October 1st – 3rd in Sestri Levante, Italy.
Training Videos
Getting Going with Materials Science Maestro Video Series
A free video series introducing the basics of using Materials Science Maestro.
Schrödinger’s Materials Science Builder Series: Disordered System Builder
The video demonstrates how to use the Disordered System Builder within Schrödinger’s Materials Science Suite to prepare systems for molecular dynamics simulations.
Schrödinger’s Materials Science Builder Series: 2D Sketcher
This video demonstrates how to use the 2D Sketcher within Schrödinger’s Materials Science Maestro for building and editing molecules, covering its toolbar, drawing area, and various palettes.
Publications
- Publication
- May 9, 2025
Efficient long-range machine learning force fields for liquid and materials properties
Weber JL, et al. arXiv, 2025, Preprint- Publication
- Mar 17, 2025
Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures
Chew, et al. npj Computational Matererials, 2025, 11, 72- Publication
- Mar 5, 2025
A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study
Zhou, et al. Nature Communications, 2025, 16, 2210Quick Reference Sheets
- Quick Reference Sheet
Coarse Grained Mapping
Get an overview of the Coarse Grained Mapping panel for mapping all-atom structures to coarse grained models.
- Quick Reference Sheet
Visualize Restraints
Get an overview of the Visualize Restraints panel for displaying restraints in a cms structure.
- Quick Reference Sheet
ML Model Manager
Get an overview of the ML Model Manager for organizing and retraining outdated ML models.
Tutorials
- Tutorial
Introduction to Materials Science Maestro Tutorial
An introduction to Materials Science Maestro, covering basic navigation, an intro to building models and several of the key functionalities of the graphical user interface.
- Tutorial
Disordered System Building and Molecular Dynamics Multistage Workflows
Learn to use the Disordered System Builder and Molecular Dynamics Multistage Workflow panels to build and equilibrate model systems.
- Tutorial
Introduction to Geometry Optimizations, Functionals and Basis Sets
Perform geometry optimizations on simple organic molecules and learn basics regarding functionals and basis sets.
Webinars
- Oct 15, 2025
難溶性薬物の放出メカニズムを解明する – ASD研究の新たなアプローチModelling amorphous solid dispersion (ASD) release mechanisms
AbbVie と Schrödinger のエキスパートが、ASDにおける薬物放出やLoss of Release のメカニズムを、熱力学モデリング・分子シミュレーション・実験研究 を組み合わせた最新の研究成果を基に解説します。
- Oct 29, 2025
Advancing battery materials innovation using charge-aware machine learning force fields
In this webinar, we will demonstrate how Schrödinger is utilizing an integrated computational approach combining physics-based molecular modeling with machine learning force fields (MLFFs) to address key challenges in battery materials design.
- Nov 13, 2025
Accelerating product development with computational materials engineering
Learn how Ansys and Schrödinger are transforming product development with Integrated Computational Materials Engineering (ICME) to accelerate material discovery and innovation.
White Papers
Latest insights from Extrapolations blog
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.
Free learning resources
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.