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.

The Global Polymer Summit

Conference

The Global Polymer Summit

CalendarDate & Time
  • September 28th-30th, 2026
LocationLocation
  • Louisville, Kentucky

Schrödinger is excited to be participating in the Global Polymer Summit 2026 conference taking place on September 28th – 30th in Louisville, Kentucky. Join us for a presentation by Croix Lancosay, Senior Scientist I at Schrödinger, titled “Design of Elastomer Monomers with Generative Machine Learning.” Stop by booth #709 to speak with Schrödinger scientists.

icon time SEPT 29 | 1:15 PM
icon location HF GROUP Theater, Expo Floor
Design of Elastomer Monomers with Generative Machine Learning

Speaker:
Croix Lancosay, Senior Scientist I, Schrödinger

Abstract:
The next generation of elastomeric materials for wire coatings and seals requires components that perform reliably under extreme conditions, placing stringent and often competing specifications on the underlying polymer chemistry. The simultaneous optimization of multiple properties remains challenging, as structural motifs that improve one property may be detrimental to another, and the vast polymer chemical space makes exhaustive experimental screening impractical. For this reason, we employ a computational workflow that combines generative AI, machine learning, and physics-based simulation to identify and validate materials that show promise as elastomeric insulators.

In this work, we apply REINVENT, a recurrent neural network-based generative framework with reinforcement learning optimization, to the inverse design of elastomer monomers targeting simultaneously two desired properties. A prior is pre-trained on a polymer database to learn the grammar of polymer chemistry, then focused toward an elastomeric chemical space. Reinforcement learning is used to steer generation toward candidates satisfying multi-objective property criteria, using Schrödinger’s built-in machine learning models as scoring functions. We evaluate generated candidates for chemical validity, diversity, and assess the most promising structures through computational property validation. This study demonstrates how an established generative AI framework can be used for goal-directed polymer discovery, and highlights opportunities for accelerating the design of specialty polymers for sealing and insulation applications.

icon time SEPT 29 | 3:30 PM
icon location Exhibit Entrance
Pop-up Talk: Rapid Polymer Design & Formulation: High-Throughput Digital Development with Schrödinger

Speaker:
Andrea Browning, Senior Director of Polymers and Soft Matter, Schrödinger

Abstract:
Schrödinger’s advanced physics-based simulation and AI-driven platform are transforming polymer research and enabling rapid screening. This presentation shows how integrating domain-informed computational tools (molecular dynamics, machine learning, agentic AI) empowers scientists to uncover root-cause material insights and streamline development workflows in a single afternoon. Learn practical approaches to embed enterprise-grade digital innovation directly into your research lab.

Biologics by the Bay – Happy Hour and Networking

Networking Event
CalendarDate & Time
  • September 29th, 2026
LocationLocation
  • San Francisco, California
Register

Join Schrödinger to learn how our computational platform accelerates biologics discovery, optimization, and formulation.

Connect with local computational biologists, protein engineers, and translational scientists to exchange ideas on how computational modeling with real-time project data can shorten design cycles, mitigate downstream risks, and foster seamless collaboration across teams.

What we will discuss:

BioLuminate: Learn how it delivers actionable insights across traditional modalities (antibodies, peptides, biocatalysts) for early liability detection, stability, affinity, and formulation. Plus, get a sneak peek at its growing capabilities for complex non-traditional modalities like ADCs and oligonucleotides.

LiveDesign for Biologics: See how this single, shared workspace connects sequence, structure, assay data, and computed properties so your team can design, iterate, and decide faster.

Bunsen: Get a quick look at our new agentic AI tool, designed to lower the barrier to entry and accelerate design cycles by making advanced computational modeling more accessible.

Networking & Happy Hour:

Stay after the presentations to connect with local industry peers, share insights, and chat with the Schrödinger team over drinks and appetizers.

Location:

Foundry & Lux, 151 Oyster Point Blvd, South San Francisco, CA 94080

Register
icon time 3:30 PM – 4:00 PM
Welcome & Registration

Location:
Foundry and Lux, 151 Oyster Point Blvd, South San Francisco, CA 94080

icon time 4:00 PM – 5:00 PM
Biologics Workflow Presentation

Speakers:
Eliud Oloo, Global Portfolio Leader, Biologics Modeling, Schrödinger

Jason Castaneda, Executive Director, Global Portfolio Leader, Schrödinger

Sunidhi Lenka, Senior Scientist II, Applications Science, Schrödinger

icon time 5:00 PM – 7:30 PM
Networking Reception

Our Speakers

Eliud Oloo

Senior Director, Global Portfolio Leader, Schrödinger

In his current role leading Schrödinger’s biologics modeling portfolio, Eliud enables R&D teams to translate advanced computational modeling insights into real-world drug discovery success. From basic and applied scientific research to product strategy, his background brings a unique cross-disciplinary perspective on solving biology’s toughest problems through computation. Previously, he headed structure-based modeling of vaccines at Sanofi, where he pioneered the application of machine learning to antigen design. He also served as Product Manager for BioLuminate, Schrödinger’s globally adopted biologics platform. Eliud is a co-inventor and co-author on multiple patents and publications in biologics design. He holds a PhD in Biomolecular Simulation from the University of Calgary, Canada.

Jason Castaneda

Executive Director, Global Portfolio Leader, Schrödinger

Jason is a Global Portfolio Leader at Schrödinger with nearly two decades of experience driving the adoption of complex scientific software platforms within major pharmaceutical and biotech firms, specializing in computational modeling and informatics platforms for drug discovery. With a background in chemical engineering, he built his career bridging scientific impact with enterprise software to transform the way companies discover new therapeutics. He now leads the global growth strategy for the portfolio of LiveDesign-based applications, the preferred platform for designing novel molecular matter in many of the world’s largest discovery organizations.

Sunidhi Lenka

Senior Scientist II, Applications Science, Schrödinger

Sunidhi Lenka is a Senior Scientist II in the Application Science team at Schrödinger. Joined in 2023, her primary focus is supporting customers in deploying the Schrödinger drug discovery suite within their workflows, alongside driving her own research and building strategic collaborations to advance work in both biologics and small molecules. Sunidhi earned her Ph.D. in Computational Chemistry from the University of Florida under Prof. Adrian Roitberg, specializing in QM/MM and enhanced sampling MD techniques for medicinal chemistry and enzyme engineering. She subsequently completed a postdoctoral fellowship at Genentech in Dr. Izadi’s lab, developing in silico tools to address high concentration viscosity challenges and chemical degradation risks (such as tryptophan oxidation and CQAs) in biologics.

Introduction to Physics and ML for ADMET Modeling

ADME Course Image, Human body

Introduction to Physics & ML for ADMET Modeling

Learn how to apply physics and ML modeling workflows to ADMET endpoints

Details
Available Languages
Chinese, English, Japanese, Korean
Duration
5 weeks from selected start date
Level
Introductory
Cost
$600 for non-student users
$160 for student / post-doc
Who should take this course?
Medicinal chemists, computational chemists, DMPK professionals

Overview

Given the multi-parameter optimization underlying all of drug discovery, the ability to efficiently profile and predict ADMET liabilities is a vital skill for both medicinal and computational chemists. This introductory course is intended to provide the foundational knowledge needed to tackle complex ADMET problems within an active project context. 

Through real-life drug discovery case studies, you will learn how to leverage a variety of physics and ML workflows to predict and optimize absorption, distribution, metabolism, excretion, and toxicity endpoints.

Ideal if you are trying to:

  • Improve your ability to collaborate and communicate about ADMET optimization between medicinal and computational chemistry teams
  • Broaden your understanding of how both physics and ML workflows can help solve complex ADMET challenges and drive project impact

 

This course comes with temporary access to a web-based version of Schrödinger software, complete with licenses and compute resources

Requirements
  • Working knowledge of organic chemistry
  • Background in drug discovery
Certification
  • A certificate signed by the Schrödinger course lead to add to your CV or resume
  • A badge that can be posted to social media, such as LinkedIn
background pattern

What you will learn

The basics of ADMET

Learn about the ADMET endpoints that are most commonly considered in small molecule drug discovery programs

Machine learning best practices

Learn how to best build and deploy machine learning models for ADMET endpoints on your programs

Physics-based modeling for ADMET

Explore and apply physics-based methods such as E-sol and Predictive Toxicology

Multi-parameter ADMET optimization

Explore a series of multi-parameter ADMET optimization case studies to simulate real-life scenarios

Modules

Module 1

Course introduction

Video
Videos
  • Course overview
  • Introduction to logD
  • Data sources and model applicability
  • The science of learning error with residual modeling
  • Transfer learning for data-limited endpoints
  • Federated learning
End checkpoint
End of module checkpoint
Module 2

Modeling absorption and distribution endpoints

Video
Videos
  • Introduction to absorption and distribution
  • Modeling for absorption endpoints
  • Modeling for distribution endpoints
Tutorial
Tutorial:
  • Optimizing EGFR binders for CNS penetration
  • Navigating solubility, hepatotoxicity, and brain penetration in a DLK inhibitor program
Tutorial
Interactive Tutorial:

Building a residual LogD model

End checkpoint
End of module checkpoint
Module 3

Modeling metabolism, excretion, and toxicity endpoints

Video
Videos
  • Introduction to metabolism and excretion
  • Modeling for metabolism and excretion endpoints
Tutorial
Interactive Tutorials:

Building a transfer learning model

Predictive toxicology

Tutorial
Tutorials
  • Multi-parameter optimization of Vanin-1 inhibitors
  • Designing in a narrow pKa window in an H3 program
Video
Videos
  • Preclinical toxicology
  • Bioavailability and dose
  • Course summary
Course completion
Course completion and certification

Need help obtaining funding for a Schrödinger Online Course?

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

What our alumni say

“I particularly appreciated the connection between theoretical principles and real-world decision-making, which helped me better understand how computational predictions can inform compound design and optimization.”
Francesco CarredaAI Scientist & Computational Antibody Engineer at mAIbe S.r.l.
“[The course] helped me know more about ADMET properties in drug discovery and how to make ML models in LD.”
Wei HuangSenior Director, CADD at Structure Therapeutics
“I liked that it covered fundamentals of ADME properties and its importance in drug discovery.”
Sarah VerhoffPhD Candidate at Emory University
“It was a very good experience to get introduced to Schrödinger and the exercise in LiveDesign.”
Judith KuntscheAssociate Professor at University of Southern Denmark

Frequently asked questions

How much does the Introduction to Physics and ML for ADMET Modeling online course cost?

Pricing varies by each course and by the participant type. For students wishing to take this, we offer a student price of $160, and $600 for non-students.

What time are the lectures?

Once the course session begins, all lectures are asynchronous and you can view the self-paced videos, tutorials, and assignments at your convenience. When registering for the course you will select the start and end date. Within those dates, you will have asynchronous access to the course material and virtual workstation to work on the course when it best suits your schedule.

How could I pay for this course?

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

Are there any scholarship opportunities available for students?

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

Will material still be available after a course ends?

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

Related courses

Introduction to Molecular Modeling for Drug Discovery Life Science Life Science
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Protein preparation, ligand docking, collaborative design, and other fundamentals of small molecule drug discovery with Maestro and LiveDesign

Applications-of-Free-Energy-Calculations Life Science Life Science
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Learn the basics of free energy calculations, their applications, and how to best integrate them into a drug discovery program

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Milano Workshop: Physics-Based and AI methods in drug discovery

Workshop

Milano Workshop: Physics-Based and AI methods in drug discovery

CalendarDate & Time
  • June 22nd-23rd, 2026
LocationLocation
  • Milano, Italy

Registration is closed for this event. We look forward to seeing you there!

Event co-organized by

University of Milan (Departments of Pharmaceutical Sciences, Pharmacological and Biomolecular sciences, and Chemistry) and  Schrödinger (sponsor of the event).

Scientific Board: Prof. Giovanni Grazioso, Prof. Ivano Eberini, Prof. Laura Belvisi, Prof. Monica Civera, Dr. Magd Badaoui

Location: aula C03, Via L. Mangiagalli 25, 20133 Milano

JUNE 22

Physics-Based Methods in Drug Discovery: a general overview of the theoretical framework and its impact on modern R&D

9.00 – 9.30 Registration and coffee

9.30 – 9.40 Introduction 

9.40 – 10.20 Targeting a bacterial lectin: from fragment-based design to covalent glycomimetic ligand, Dr. Giulia Antonini

10.20 -11.00 Nature 2.0: rewriting the rules of peptide and proteins, Dr. Enrico M. A. Fassi

11.00 -11.20 Coffee break

11.20 -12.00 Molecular modeling tools for protein structures study, Dr. Omar Ben Mariem

12.00 -13.00 The Schrödinger ecosystem: a deep dive into how these methods are translated into actionable tools within our platform, Dr. Magd Badaoui

13.00 – 14.30 Lunch

14.30 – 17.30 Hands-on session by Dr. Magd Badaoui (Schrödinger):

  • Maestro & PyMOL Essentials: Getting started with the interface and generating high-quality visualizations.
  • Ligand Designer Workshop: Interactive session on real-time ideation and pocket-aware design.
  • Accelerating Lead Optimization: From Structure to Insights – A comprehensive workflow for refining hits into candidates.

Location: aula Alfa and aula Beta, Via Celoria 18, 20133 Milano

JUNE 23

AI in drug discovery

8.30 – 9.00 Registration and coffeee

9.00 – 10.00 Introduction to Machine learning and AI methods, Dr. Marco Nicolini

10.00 – 10.45 Generative AI in Biotech: Hands-On De Novo Nanobody Design, part 1, Prof. Alessandro Contini

10.45 – 11.15 Coffee Break

11.15 – 12.00 Generative AI in Biotech: Hands-On De Novo Nanobody Design, part 2, Prof. Alessandro Contini

12.00 – 13.30 Introduction to QSAR and AI-Based Classification Methods in Drug Discovery, Dr. Angelica Mazzolari

13.30 – 14.30 Lunch

14.30 – 15.30 From Sequence to Structure: pros and cons of AlphaFold-Based Modeling, Dr. Omar Ben Mariem and Dr. Luca Palazzolo

15.30 – 18.00 Machine Learning with Schrödinger’s DeepAutoQSAR, theory and practice by Dr. Magd Badaoui (Schrödinger)

Events for registered PhD students and Research Fellows.

Scientific Board: Prof. Giovanni Grazioso, Prof. Alessandro Contini, Prof. Giorgio Valentini, Dr. Angelica Mazzolari, Dr. Omar Ben Mariem, Dr. Enrico Fassi, Dr. Marco Nicolini, and Dr. Magd Badaoui

Location: Aula C13, Via L. Mangiagalli 25, 20133 Milano

IUCr2026

Conference

IUCr2026

CalendarDate & Time
  • August 11th-18th, 2026
LocationLocation
  • Calgary, Alberta, Canada

Schrödinger is excited to be participating in the IUCr2026 conference taking place on August 11th – 18th in Calgary, Alberta, Canada. Join us for a presentations by Schrödinger scientists.

icon time AUG 12 | 12:10PM
A robust CSP platform for predicting crystal polymorphs of pharmaceuticals, agrochemicals, petrochemicals and energetic materials

Speaker:
Shiva Sekharan, Global Portfolio Leader of Formulations/CSP, Schrödinger

Abstract:
The ability of an active ingredient (AI) in pharmaceuticals, agrochemicals, petrochemicals and energetic materials to exist in multiple crystalline forms, which significantly affects solubility, stability, downstream processing, bioavailability, and efficacy is called crystal polymorphism. The synthesis and experimental testing of polymorphism in small molecules can be time consuming, expensive and even hazardous, but their many industrial applications necessitate their constant research and development. Over 50% of AIs exhibit polymorphism, requiring careful control during manufacturing to avoid inconsistent performance during formulation. A less stable (metastable) form may dissolve faster, while a more stable form may have better shelf life. We have developed a robust crystal structure prediction (CSP) platform that leverages a novel, systematic crystal packing search and a hierarchical energy ranking protocol. By integrating machine learning force fields with molecular dynamics and quantum mechanics, the platform efficiently generates and identifies the most stable crystal polymorphs directly from molecular structure. Access to our CSP platform is available via the Schrödinger’s Materials Sciences and Life Sciences software suite as well as via service engagements.

icon time AUG 17 | 4:30PM
Structure-based drug design for ion channels and transporters

Speaker:
Abba Leffler Senior Principal Scientist, Computational Chemistry, Schrödinger

Abstract:
Cryogenic electron microscopy (cryo-EM) has revolutionized the study of ion channels and transporters by facilitating determination of their structures, states, and ligand-bound conformations. However, it remains unclear how best to utilize these structures effectively for drug discovery. In this talk, I will describe the results of a large-scale effort to benchmark gold-standard potency prediction methods such as free-energy perturbation for an array of clinically relevant ion channels and transporters using publicly available structures and structure-activity relationship data. I will discuss the effectiveness of these calculations for prioritizing potent compounds, the role that an explicit membrane representation plays in their accuracy, and the extent to which so-called “activity cliffs” can be rationalized by displacement of high-energy waters. Time permitting. I will also give an overview of our germane experiences on early discovery programs and in the context of our predictive toxicology efforts.

Release 2026-2

Library Background

Release Notes

Release 2026-2

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Interactive 2D Overlay: Precision marquee and lasso selection on the 2D Overlay that synchronizes instantly to the Workspace, bypassing the visual clutter of the 3D binding site.
  • Task Tool Smart Search: Intelligent, fuzzy search that understands user intent – instantly locating the desired panels even with typos or conceptual queries like ‘dockng’ or ‘T-cell’
  • Maestro Assistant:
    • Agentic Status Indicators: Real-time status updates replace the generic loading animation, so you always know what the assistant is doing.
    • Multiple persistent chat sessions – Users can now create, rename, switch between, and delete multiple chat sessions, each maintaining its own conversation history.
    • Privacy & Transparency Controls: Users can now enable or disable the Maestro Assistant directly from Preferences, with clear in-UI communication of the current privacy mode. The updated welcome screen and persistent AI disclaimer footer reinforce that your workspace structures are never sent to the LLM.
  • Surfaces: Projecting ESP and Fukui reactivity maps onto Jaguar-computed electron density isosurfaces is now a single-click action, where previously it required multiple manual steps.

Force Field

  • Improvement in the accuracy of metal complexes and metal-ligand interactions of specific metal ions (Ca(II), Mg(II), Fe(II), Zn(II), Pt(II), Ir(III)) from the addition of the metals polarizable force field via OPLS5. Current application support is in relative/absolute binding FEP+ and Desmond (beta).

Target Validation & Structure Enablement

Protein Preparation

  • Improved ease of use to assess clashes with crystal mates in the Diagnostics panel
  • New and improved side-chain reconstruction algorithm
  • Better logging of use and matching of FASTA sequences to entry
  • Academic Maestro can now minimize prepared structures
  • Automatically fix bonding of sulfoximines, which are often incorrectly assigned in the wwPDB/CCD 
  • New option to automatically add glycosylation bonds in Maestro 
  • Detect ring spears during preprocessing, displaying them in the ‘Overlapping’ Diagnostic pane

Predictive Tox Panel

  • Full release of the Predictive Tox panel for screening unknown liabilities
  • Open beta release of the Predictive Tox SAR panel for automated production of high accuracy atomistic models of common anti-targets using existing SAR

Binding Site & Structure Analysis

Mixed Solvent MD (MxMD)

  • Save a manual step with automatic execution of SiteMap rescoring as part of MxMD simulations
  • Density maps are now written in ccp4 format instead of cns

Hit Identification & Virtual Screening

Docking

  • The high-accuracy MacroDock macrocycle docking workflow is now available from the command line
  • Enhanced performance and efficiency in ultra-large library screening with Generative Glide (Beta): Efficiently screen ultra-large on-demand synthesizable libraries with generative AI in a fraction of the time as AL-Glide and brute force approaches

Ligand Preparation

Enumeration

  • New interface to enumerate novel molecules using generative AI for molecular materials with the REINVENT method

Shape Screening

  • QuickShape now stores oned_screen results as .csv

ABFEP

  • Apply restraints from the interface

Lead Optimization

FEP+

  • Identify the optimal simulation time to maintain accuracy with time-sliced statistics
  • Share FEP+ protocols across Web Services (GraphDB), Active Learning FEP, and the FEP+ Protocol Builder for consistency with easy import and export of new FEP+ protocol configuration files
  • Improved Force Field parameter merging
  • Improved cycle-closure calculation performance by up to 100x by saving subcycles and graph traversal

Protein FEP

  • Renamed protein “selectivity” to “affinity” to be consistent with residue scanning

FEP+ Protocol Builder

  • Account for “grouped” compounds when assigning test/train splits

Macrocycles

  • Macrocycle Score: New semi-automated workflow to optimize and score macrocyclic cores for a given set of conformers, with support for high-fidelity scoring with MLFFs and QM
  • Macrocycle Stability: New script for using binding pose metadynamics to rank macrocyclizations for stability compared to a reference molecule
  • Macrocyclize: More detailed rejection reasons are now reported for cyclization reactions
  • Macrocyclize: Automated enumeration of all L- and D-amino acid attachment pairs for custom SMILES linkers in peptide mode
  • Prime Macrocycle Sampling: New command-line argument, -use_random_seed, to set a random seed during sampling
  • Prime Macrocycle Sampling: New command-line argument, -curvature_tol, to specify curvature restraints during sampling
  • Prime Macrocycle Sampling: New command-line argument, -freeze_torsions, to specify torsions to freeze during sampling

De Novo Design

AutoDesigner – R-group Design

  • Full release of R-group Design panel
  • Improved AutoDesigner PDF report

AutoDesigner – Core Design

  • Full release of Core Design panel
  • Improved AutoDesigner PDF report

AutoDesigner – Linker Design

  • Full release of Linker Design panel
  • Improved AutoDesigner PDF report

Drug Formulations

  • Formulation ML: Support for proteins

Crystal Structure Prediction

  • Full release of Crystal Structure Prediction with support of anhydrous Z’=2 and monohydrate crystal polymorph structures: Identify stable crystal polymorphs at zero Kelvin and room temperature for a given compound through hierarchical scoring

Docs Content

  • Filter tutorials based on interest
  • Materials Science Panel Explorer URLs retain filter states
  • New interactive GPU licensing calculator
  • Collapsible sidebar menu

Education Content

  • New Tutorial: Filtering and Validating Protein-Protein Docked Poses with Macromolecular Pose Filtering and MM-GBSA Residue Scanning
  • New Tutorial: Introduction to Performing Metadynamics Simulations with Desmond
  • New Tutorial: Screening Ultra-large Libraries with Generative Glide
  • New Tutorial: Peptide Cyclization
  • New Tutorial: Preparing Cyclic Peptides and Aligning them to a Reference Structure
  • New Interactive Tutorials: Three interactive tutorials for AutoDesigner, a de novo design algorithm for rapidly exploring large chemical space for lead optimization
    • Set up Linker Design with AutoDesigner in Maestro 
    • Set up Core Design with AutoDesigner in Maestro
    • Set up R-Group Design with AutoDesigner in Maestro

Biologics Drug Discovery

  • Non-standard amino acids script (Open Beta). A command line script to simplify and speed creation of non-standard amino acid databases for residue scanning.
  • Introduced a “Fast Mode” command line option for calculating protein descriptors. The new mode speeds up computation of these descriptors by about 20% on a benchmark dataset. Especially useful for large ensembles of structures.
  • Enabled light-chain template subtype selection during antibody modeling. This provides greater flexibility when selecting templates for generating antibody structural models.
  • Added the lengths of antibody Complementarity-Determining Regions (CDRs) to the output of calculated protein descriptors for antibody profiling analyses.

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Reduced disk space usage for calculating partial charges
  • Bader charge analysis (command line, requires Badar module installation)
  • Parallelization over both bands and tasks (command line)
  • Support for MLFF in NEB calculations

Defect Properties:

Product: MS DefectPro

  • Defect Formation Energy Diagram: Analysis panel for defect energy

Microkinetics

Product: MS Microkinetics

  • Improved UI for renaming reactions
  • User control of displayed stage in the Final State tab

Active Learning Optoelectronics

Product: Active Learning Optoelectronics

  • (+MATSCI_OPTO_AL_ML) Access to ML property prediction models
  • Prediction for fluorescence

Reactivity

Product: MS Reactivity

  • Reaction Network Viewer: Color scheme to identify reactants
  • Nanoreactor: Setup for number of stored structures to calculate biasing potential (command line)
  • Nanoreactor: Improved sampling of input conformations
  • Nanoreactor: Option to use AutoTS for transition state analysis

Crystal Structure Prediction

Product: Crystal Structure Prediction

  • Crystal Structure Prediction: Support for Z’=2 and monohydrates

Advanced Force Field Applications

Product: MS FF Applications

  • Infrastructure: Support for metals with OPLS5 from the materials science workflows (open beta)

Transport Calculations via MD simulations

Product: MS Transport

  • Diffusion Coefficient: Support for multi-component diffusion analysis
  • Ionic Conductivity: Output report in the driver log for command-line access
  • Thin Plane Shear: Support for viscosity prediction from velocity profile

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Coarse-Grained Mapping: Standardized color scheme for Martini protein residues
  • Backmapping: (+COARSE_GRAIN_BACKMAPPING) Algorithm to map coarse-grained structures to all-atom models
  • Coarse-Grained FF Assignment: Automatic setup of restraints for DPD mapping
  • CG FF Builder: (+CGFF_BUILDER_PARAM_NO_RESPONSE) Improvements to fitting algorithms for bond and angle terms
  • CG FF Builder: Martini type guesses made visible to users
  • Particle and residue names visible in MS Maestro
  • Improved Martini parameters for linear polysaccharides

Complex Bilayer Builder

Product: MS Complex Bilayer Builder

  • Membrane Analysis: Automated head/tail assignment independent of pdb names

Formulation ML

Product: MS Formulation ML

  • Formulation ML: Support for protein
  • Formulation ML: Support for aggregation functions over ingredient descriptors

Layered Device ML

Product: MS Layered Device ML

  • Support for using existing device models as descriptors for new device models

Generative AI

Product: MS Generative AI

  • REINVENT: Generative AI for molecular materials using the REINVENT method

MS Maestro Builders and Tools

  • Disordered System: Support for barrier potential with planar interface substrates
  • Interface Builder: (+INTERFACE_BUILDER) Advanced solution for building interface models
  • Materials Project: API key from materialsproject.org hidden from the panel UI
  • Meta Workflows: Support for order parameter analysis
  • Meta Workflows: Support for bulk macromolecule relaxation protocol
  • Periodic Structure Enumeration: Option to rank by electrostatic energy
  • Polymer: Speed-up for building ladder polymers
  • Remove Molecules from System: Speed up of up to 100X for loading CG systems
  • MD Multistage: Improvements to built-in relaxation protocol ‘Semicrystalline 1’
  • Solvate System: Option to prevent molecular splits across periodic boundaries
  • Sugar Builder: Stand-alone model building solution for common polysaccharides

Classical Mechanics

  • Droplet: Coarse-grained structures enabled
  • Electrolyte Analysis: Support for 2D distribution analysis of coordinating shells
  • Electrolyte Analysis: Support for cluster composition analysis
  • Electrolyte Analysis: Support for residence time analysis
  • Electrolyte Analysis: A tab in the viewer panel to summarize analysis results
  • Electrolyte Analysis: Display of RDF and structure factor plots
  • Polymer Crosslink: Support for NpγT and NpAT ensembles
  • Polymer Crosslink: Support for opening multiple viewer windows
  • Surface Tension: (+SURFACE_TENSION_CG) Support for coarse-grained models
  • Thermophysical Properties: Option to stop after a series of failed simulations
  • Thermophysical Properties: Support for varying MD timesteps by temperature
  • Thermophysical Properties: Display of Calibrated Tg value from the viewer panel
  • Thermophysical Properties: Support for MLFF
  • Umbrella Sampling: Option to export potential of mean force to CSV output

Quantum Mechanics

  • Adsorption Energy: Atomic constraints setup for molecular QM calculations
  • Adsorption Energy: Improved interface for setting atomic constraints
  • Optoelectronic Film Properties: Option to select the excited states of interest for ISC/RISC
  • Optoelectronic Film Properties: Improved parallelization for ISC/RISC
  • Optoelectronic Film Properties: Geometry optimization settings for ISC/RISC

Docs Content

  • Filter tutorials based on interest
  • Materials Science Panel Explorer URLs retain filter states
  • New interactive GPU licensing calculator
  • Collapsible sidebar menu

Education Content

  • New Tutorial: Protein Characterization: Part 1
  • New Tutorial: De Novo Design of Novel Compounds with REINVENT
  • New Tutorial: Machine Learning for Formulation Containing Proteins
  • Updated Tutorial: Droplet Contact Analysis
  • Updated Tutorial: Nanoreactor
  • Updated Tutorial: Liquid Electrolyte Properties: Part 2
  • Updated Tutorial: Defect Formation Energy Calculation
  • Updated Tutorial: Crystal Structure Prediction
  • Quick Reference Sheet: Sugar Builder

Education Content

Life Science

  • New Tutorial: Filtering and Validating Protein-Protein Docked Poses with Macromolecular Pose Filtering and MM-GBSA Residue Scanning
  • New Tutorial: Introduction to Performing Metadynamics Simulations with Desmond
  • New Tutorial: Screening Ultra-large Libraries with Generative Glide
  • New Tutorial: Peptide Cyclization
  • New Tutorial: Preparing Cyclic Peptides and Aligning them to a Reference Structure
  • New Interactive Tutorials: Three interactive tutorials for AutoDesigner, a de novo design algorithm for rapidly exploring large chemical space for lead optimization
    • Set up Linker Design with AutoDesigner in Maestro 
    • Set up Core Design with AutoDesigner in Maestro
    • Set up R-Group Design with AutoDesigner in Maestro

Materials Science

  • New Tutorial: Protein Characterization: Part 1
  • New Tutorial: De Novo Design of Novel Compounds with REINVENT
  • New Tutorial: Machine Learning for Formulation Containing Proteins
  • Updated Tutorial: Droplet Contact Analysis
  • Updated Tutorial: Nanoreactor
  • Updated Tutorial: Liquid Electrolyte Properties: Part 2
  • Updated Tutorial: Defect Formation Energy Calculation
  • Updated Tutorial: Crystal Structure Prediction
  • Quick Reference Sheet: Sugar Builder

LiveDesign

What’s New in 2026-2

  • Project-Level Scaffolds: Project admins can publish scaffolds from the R-group decomposition panel as a project-wide template, which users can import into any LiveReport’s R-group decomposition.

  • Customizable Project Dashboards: customize the Project Dashboard Overview page by adding and arranging configurable widgets—including LiveReport Bookmarks, Entities, Resources, Comments, LiveReports, Templates, Project Milestones, and Plots

  • (Beta) Multi-Entity Matrix Forms Widget: add a Multi-Entity Matrix widget to Forms layouts to view compound data in a transposed table, with compounds as column headers and assay, R-group, and model data as rows. Coordinate with your Schrodinger representative to enable this feature.

  • Biologics

    • Toggle between atomistic and monomeristic rendering for biologic entities with up to 200 heavy atoms from the first-column entity image.

    • Design single-point mutants using custom monomers: custom and unnatural amino acid monomers now appear in the Mutations dropdown for residue substitution.

    • View and manage your Monomer Database with role-based access: Project Admins and Admins can create, edit, and delete monomers, while other users have view-only access; use the new list_monomers method in LDClient to query and filter monomers programmatically.

    • Search within annotated antibody regions: the subsequence search query now supports structure annotation, allowing searches within auto-annotated regions of an antibody; pair with an Entity type query to narrow the search scope.

    • Filter the Sequence Viewer by Framework regions alongside CDRs to support humanization, germline comparison, and liability assessment workflows.

    • Show all linkages in the Sequence Viewer to display covalent connection points for biologic sequences, including cyclic and branched peptides.

    • View logo plots in the Sequence Viewer to visualize amino acid conservation across aligned biologic sequences, with residues colored by their coloring scheme.

    • Residue sync selection between the 3D Visualizer and Sequence Viewer now works for structures uploaded as GE entities.

  • 3D Visualization

    • Align structures in the 3D Visualizer by selecting a reference structure and aligning one or more mobile structures to it.

    • Pick custom colors for entire 3D structures in the Contents pane, available for GE structures and structures uploaded via Image/File Freeform columns.

  • Advanced Search

    • Search by preferred alias using the new ID column query in the Advanced Search panel; ID queries can be combined with other query types, used within search groups, and set to Match by Child or Match by Property.

    • The Advanced Search complex view now includes Real/Virtual quick filter options and an Entity type dropdown for filtering by compounds, R-groups, biologics, and other entity types.

  • RetroSynth

    • Improved performance with expanded building block coverage (170 billion building blocks) and search and route ranking improvements.

    • Simplified model paradigm: RetroSynth now uses a single protocol per instance rather than one per project; models remain project-specific. Note: all existing RetroSynth models have been archived as part of this change — add the RetroSynth model again to any LiveReports where it was previously configured. Requires LiveDesign ML.

  • Plots: Aggregate Dates: group date-axis data by days, weeks, months, quarters, or years, and aggregate Y-axis values by Mean, Median, Count, or Sum; optionally show empty date bins for consistent axis spacing.

  • Alias Model and Other Columns: assign custom display names to model columns and columns in the “Other” section of the Data & Columns tree at the project level (by project admins) or at the LiveReport level (by any user); aliases appear in column headers, filters, plot axes, and the Data & Columns tree, with the original system name visible in the column header tooltip.

  • UX Improvements

    • Admin users can download a sample CSV template from the Bulk Add Users page to guide user upload file preparation.

    • The email field is now optional on the Update Users page in the Admin Panel.

    • The SSO login page now also displays username and password fields, allowing users to log in with standard credentials when SSO is unavailable.

What’s Been Fixed

  • Advanced Search

    • Pasting IDs into the All IDs filter query box in complex view would also paste text outside the box and produce incorrect filtering results, and now IDs are pasted only within the filter query box.

    • Creating a new query group from an independent query in Advanced Search complex view would insert it out of order, and now appends the new group in the correct position.

    • Auto-update search for biologics would not include newly imported entities until the search was manually rerun, and now fetches matching entities as they are imported.

    • Toggling AND/OR logic between conditions in an Advanced Search query group would not switch to complex view as expected, and now correctly transitions to complex view when mixed logic is introduced.

    • New queries added in Advanced Search complex view would appear at a random position rather than at the cursor location, and now are inserted at the cursor position.

    • Creating a new query group via the cog menu in Advanced Search complex view would insert it at a random position, and now adds it in the correct location relative to the source query.

    • Toggling AND/OR logic between sub-queries within a query group in Advanced Search simple view would not apply the change, or would update all conditions simultaneously, and now correctly updates only the selected logic.

    • Deleting query groups containing subsequence queries in Advanced Search would throw an error that persisted even after a browser refresh, and now deletes without error.

    • Flipping AND/OR logic inside a query group with only 2 queries would unexpectedly switch the view from simple to complex, and now stays in simple view.

    • Using parentheses to group conditions inside a query group in Advanced Search complex view would always show an Invalid Search error, and now correctly allows valid groupings to be searched.

    • Advanced Search queries using date Freeform columns would fail to return results, and now searches correctly.

    • The cancel button now appears when casting a new vote.

    • Adding a plot to the Project Dashboard overview page would sometimes fail on the first attempt, and now adds reliably.

  • Filters

    • Switching from complex to simple filter view would incorrectly warn that the filter expression was too complex in some cases, and now shows that warning only when the filter genuinely cannot be represented in simple view.

    • The filter view would automatically switch from complex to simple when a column with an active filter condition was removed from the LiveReport, and now retains the complex view.

  • Formulas

    • 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.

    • Formulas using date functions on columns with multiple values per compound would cause all cells in the LiveReport to blink and become unusable, and now calculate correctly.

    • Formulas referencing an aggregated assay column would show ERROR instead of calculating, and now compute correctly for all aggregation modes.

    • Formulas using assay inputs whose first value was null would return ERROR instead of a result, and now calculate correctly.

    • The aggregateMax() and aggregateMin() formula functions now return blank cells instead of ERROR when the input column contains no values for a given compound.

    • The formula editor would show ‘Column not in LiveReport’ instead of the actual column name when a dependent column had been removed from the LiveReport, and now displays the column name correctly.

  • Data & Columns Tree

    • The Data & Columns tree would sometimes show only certain sections after typing and clearing a search term, and now correctly resets to show all sections when the search is cleared.

  • LiveReport

    • Duplicating, copying to a project, or saving a LiveReport as a template would fail when Advanced Search contained a subsequence query inside a query group, and now completes successfully.

    • Opening a LiveReport would load data for all plots in that LiveReport, causing browser performance issues, and now loads only the data needed for the active view.

    • Columns with long header text would not be resizable when a scroll bar appeared in the header, and now column width and height can be adjusted regardless of whether a scroll bar is present.

    • LiveReport subscription email notifications would not be delivered to users who authenticate via SSO, and now correctly send email notifications to SSO users.

    • Applying a template to a LiveReport that had previously had a template applied would silently fail with no changes made, and now applies correctly regardless of prior apply template history.

    • SVG images in Freeform columns and model result cells would display as blank cells instead of the image, and now display correctly across Chrome, Firefox, and Edge.

    • Column group names in the LiveReport header are now correctly centered.

    • Freezing or pinning a row would cause cells in that row to blink indefinitely until the browser was refreshed, and now rows freeze and pin correctly without persistent blinking.

    • The indicator showing that comments exist in a different mode (row-per-experiment or row-per-lot) was missing from comment column cells, and now appears correctly when switching between modes.

    • Switching to Kanban view would cause tiles to display flashing cells instead of data, and now correctly loads results for LiveReports with fewer than 1000 compounds.

    • Child entities with multiple lots created via the Entity Grouping Manager would continuously shift position in Row-per-Lot mode, and now remain stable.

    • Limited assay columns with filtered-out values would display alignment lines without proper spacing, breaking intracell alignment for multi-value cells, and now display with correct spacing.

    • Users assigned as editors to a read-only comment column could not add or edit comments, and now have the expected edit access in those columns.

  • Maestro

    • Column names containing < or > characters would be truncated at those characters when importing data into Maestro, and now the full column name is preserved.

  • Admin Panel

    • Atom font size and bond line width options are now available again in the Admin Panel Properties page.

    • Users created via bulk upload would not be assigned the license status specified in the CSV file, and now correctly inherit license and role settings from the uploaded file.

    • Assigning a license to an SSO user migrated from LDAP would fail if the user had no email address on file, and now succeeds with a notification displayed when no email is available.

    • Users created via bulk upload would lose their assigned roles if any field was edited before saving, and now retain their roles regardless of edits made during the import.

  • Authentication

    • Logging in via SSO would intermittently show an error page in some configurations, and now correctly completes the SSO login flow.

    • Logging in via SSO would sometimes trigger an immediate logout followed by a repeated login/logout loop, and now completes login without looping.

    • Users would be logged out due to the idle timeout even while actively using LiveDesign in another browser tab, and now activity in any tab resets the idle timer across all open tabs.

    • Users would be logged out prematurely when the browser suspended an idle tab due to a network interruption, and now remain logged in through brief network disruptions.

  • Biologics & Sketcher

    • Some toolbar buttons in the Sketcher would display in dark mode while others did not, and now all toolbar buttons display with a consistent appearance.

    • Structure images generated as SVGs would display an inconsistent font that did not match the Sketcher, and now correctly use the expected font.

  • 3D Visualization

    • Lasso selection in the 3D Visualizer would select residues and atoms outside the drawn area, and now correctly selects only the residues and atoms within the lasso area.

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

MS DeNovoML

MS DeNovoML

A generative AI interface for autonomous molecular design to accelerate the discovery of next-generation materials with optimized performance profiles

MS DeNovoML uses state-of-the-art generative AI to autonomously produce novel molecular structures optimized for specific performance criteria and material properties. The platform provides a streamlined graphical environment, enabling scientists to navigate complex chemical spaces and balance multiple design objectives, including incorporating predictive DeepAutoQSAR models and enforcing physical constraints such as molecular weight and chemical composition.

DeNovoML effectively bridges the gap between high-level generative modeling and practical materials science, delivering AI-driven property optimization and design exploration to users at all levels.

background pattern

Key Capabilities

GUI-powered REINVENT integration

that transforms the REINVENT generative engine into an accessible, panel-based workflow for code-free molecular and materials design

Seamless DeepAutoQSAR model support

to plug in custom-trained predictive models that provide real-time scoring of generated molecules against target properties

Granular structural and property filters

to enforce essential boundaries on molecular weight, molecular descriptors, and specific chemical features to ensure candidates meet project specifications

Autonomous molecular generation using reinforcement learning

to explore vast chemical spaces and generate high-quality candidates that satisfy precise functional requirements

Broad applications across materials science research areas

Tutorial

REINVENT for designing novel heat transfer fluids

Related Product

DeepAutoQSAR

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

MS Informatics

Automated machine learning tools for materials science applications

Schedule a demo on MS DeNovoML

Form submitted

Thank you, we’ll be in touch soon.

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.

MS DefectPro

MS DefectPro

Calculate electronic and structural properties of point defects in solid state materials
MS DefectPro

Point defects, such as vacancies, substitutions, interstitials, and antisites can – even at low concentrations – significantly impact the electronic, optical, structural, and mechanical properties of crystals. Therefore, predicting defect concentrations and understanding which defect types are predominant are crucial in solid state materials science. MS DefectPro provides a comprehensive platform for predicting defect formation energies and related properties.

Key Capabilities

Build atomistic structures of point defects such as vacancies, substitutions, interstitials, and antisites within a known crystal structure

Calculate defect formation energy with Density Functional Theory as a function of charge state and chemical potential, including correction energy for charged defects

Visualize defect formation energies, charge distribution, and spin density

Broad applications across materials science research areas:

Tutorial

In this tutorial, we will learn how to generate point defects and how to calculate their defect formation energy, which includes a correction term for charged defects. Additionally, we will have a brief look at the density of states and the projected density of states.

Related Product

MS Maestro

Complete modeling environment for your materials discovery

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Schedule a demo on MS DefectPro

Form submitted

Thank you, we’ll be in touch soon.

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.

Frontiers in Digital Chemistry: Korea Industry Summit

Summit
CalendarDate & Time
  • June 25th, 2026
LocationLocation
  • Pangyo, Korea

그래비티 조선 서울 판교—경기 성남시 분당구 판교역로 146번길 2 지하1층

Register

신청서 작성 후, 참석확정 메일을 받으시면 등록이 완료됩니다.

슈뢰딩거 코리아에서 재료과학(Materials Science) 분야의 전문가분들을 모시고 Frontiers in Digital Chemistry: Industry Summit을 개최합니다.

본 행사에서는 Machine learning, Polymer, Catalyst, Battery와 OLED, 그리고 첨단 소재를 포함한 다양한 산업 분야의 과학자, 기술 리더 및 R&D 의사 결정권자들이 모이는 자리입니다. 인공지능(AI), 물리 기반 모델링, 그리고 컴퓨팅 워크플로우의 통합이 어떻게 재료 혁신을 재편하고 발견을 가속화하고 있는지 심도 있게 탐구할 예정입니다.

참석 대상

슈뢰딩거 소프트웨어 사용 여부와 관계없이 다음 분야에 종사하는 산업 전문가라면 누구나 환영합니다.

  • 소재 연구 및 개발 (R&D) 부문
  • 계산 화학 및 분자 모델링 전문가
  • 소재 과학 분야의 AI 및 머신러닝 적용 실무자
  • 산업 R&D의 디지털 전환(Digital Transformation) 담당자
  • Machine learning, Polymer, Catalyst, Battery, OLED, 그리고 첨단 소재 및 관련 산업계 혁신 리더

Agenda

Register

Frontiers in Digital Chemistry: Tokyo Pharmaceutical Formulation Workshop Event

CalendarDate & Time
  • June 30th, 2026
LocationLocation
  • Tokyo, Japan

Tokyo | Pharmaceutical Formulation Workshop
次世代デジタル製剤開発 実践ワークショップ
June 30 2026

この度、シュレーディンガーが開催するフラッグシップイベント「Frontiers in Digital Chemistry」のPharmaceutical Formulation Workshop「次世代デジタル製剤開発 実践ワークショップ」を2026年6月30日に開催する運びとなりました。

本セッションは、欧州のグローバル企業の研究者たちから絶賛されたプログラムを日本向けに再構築したものです 。
単なる聴講型のセミナーではなく実践的なワークショップをご用意しております。

既にご利用いただいている皆様にとっては新たなインサイトを見出す1日に、これから利用を検討される皆様にとっては、今後の可能性を評価し戦略に組み込むことのできる機会となりますよう、弊社一同、皆様のご参加を心よりお待ち申し上げます。

導入レクチャーのアブストラクトはこちらからご覧いただけます。

icon time 9:30-10:00
受付・Welcome coffee

icon time 10:00-10:10
ご挨拶

icon time 10:10-10:45
Accelerating Materials Development with Physics and AI: New Capabilities for Pharmaceutical Formulation in the Schrödinger MS Platform

Mathew D. Halls, Senior Vice President, Materials Science

icon time 10:45-11:15
Leveraging Schrödinger’s Formulation Tools and Automated Workflows to Mitigate Technical Risks in Small Molecule Formulation

Shiva Sekharan, Global Portfolio Leader of Formulations/CSP

icon time 11:30-12:30
Workshop 1 – Automated workflows (pKa Prediction with Macro-pKa) spectroscopy, catalysis

pKa予測、触媒化学、分光学解析に加え、実務でニーズの高い「分解物予測」などの発展的アプローチを習得。

icon time 12:30-13:30
ランチ

icon time 13:30-14:30
Workshop 2 – Predictive modeling ASD model building, Excipients, Polymer screening/ Tg property prediction

ASD(非晶質固体分散体)における賦形剤・ポリマーの高精度なスクリーニングや、ガラス転移温度(Tg)の予測手法を解説。

icon time 14:30-15:30
Workshop 3 – Machine learning workflows (Solvent, coformer screening)

溶液系における機械学習(AI)を用いた最新の解析手法を解説。

icon time 15:30-16:30
Workshop 4 – Biologics Formulation Simulating Complex Protein Solutions Creating a Coarse-Grained Model for Protein Formulations

タンパク質の溶液中での分子としての挙動をシミュレーション。バイオ医薬品の添加剤等のスクリーニングへの応用を想定。

icon time 16:30-17:00
フィードバック・Q&A

icon time 17:00-18:30
レセプション

【本イベントならではの特別プログラム】
理論と実践を繋ぐ「ハンズオンセッション」
弊社マテリアル部門 シニア・バイス・プレジデントの Mathew D. HallsとグローバルポートフォリオリーダーのShiva Sekharanから最新アプローチをご紹介します 。また、弊社サイエンティストの直接サポートのもと、実際のツールに触れながらプロセス(How)をご体感いただくワークショップをご用意しております。弊社ソフトウェアの利用経験がない場合でも、実践的な体験ができるようサポートいたしますので、ぜひお気軽にご参加ください。

ワークショップ参加特典
ワークショップに参加いただいた方限定で、弊社ソフトウェア1ヶ月間評価ライセンスや、オンライントーレニングコース受講クーポンを進呈。ご自身のR&Dプロジェクトで直ちにシミュレーションを評価、お試しいただけます。(※評価ライセンス付与、受講クーポン発行については諸条件がございます。詳細については、弊社担当までお問い合わせください)

英語セッションも安心のサポート体制
英語で実施されるセッションについては、翻訳資料の提供を予定しています。また、日本人サイエンティストが同席し、質疑応答もサポートいたします。

【 開催概要】

  • イベント名: Frontiers in Digital Chemistry | Formulation Workshop
    「次世代デジタル製剤開発 実践ワークショップ」
  • 日時: 2026年6月30日(火)10:00-18:30 (レセプション17:00-18:30)
  • イベント形式:会場開催です。オンライン配信はございません。
  • 会場: 東京ミッドタウン八重洲 5F 八重洲ミッドタウン カンファレンス
  • 参加費: 無料(事前登録制)

※登録受付は6月22日(月)23:59までといたします。
※会場の収容可能人数には限りがあり、登録受付期日前であっても、上限に達し次第締め切りとなります。お早めにお申し込みください。
※参加者様へは、別途メールにて詳細をご案内いたします。

【お申込みにあたって】
所属企業または所属機関のメールアドレスにて、登録をお願いします。
フリーメールや個⼈メールアドレスでご登録の場合などは、出席をご遠慮いただく場合がございます。
同業他社さまには参加をご遠慮頂いております。ご理解のほど宜しくお願い致します。

※ご質問、ご不明な点がございましたら下記までお問い合わせください。
シュレーディンガー株式会社 FIC2事務局
E-mail: info-japan@schrodinger.com

17th Global Drug Delivery & Formulation Summit

Conference

17th Global Drug Delivery & Formulation Summit

CalendarDate & Time
  • May 18th-20th, 2026
LocationLocation
  • Berlin, Germany

Schrödinger is excited to be participating in the 17th Global Drug Delivery & Formulation Summit taking place on May 18th – 20th in Berlin, Germany. Join us for a presentation by John Shelley, Fellow at Schrödinger, titled “Molecular Modeling and Machine Learning for Small Molecule and Biologic Drug Formulation.” Stop by booth #4 to speak with Schrödinger scientists.

icon time MAY 18 | 15:35
icon location Room 3
Molecular Modeling and Machine Learning for Small Molecule and Biologic Drug Formulation

Speaker:
John Shelley, Fellow at Schrödinger

Abstract:
Selecting and combining the right ingredients in the appropriate manner is essential for successful drug formulation given the inherent challenges and competitive market. With advances in modern machine learning, physics-based simulation techniques and computer hardware, modelling is emerging as a valuable source of information that complements experimental characterization.  We showcase a cross-section of capabilities within Schrödinger’s Suite for modeling related to formulations of small-molecule or biologic drugs.  For small-molecule drugs workflows have been created for characterizing crystal polymorphs, crystal morphology and degradation risks as well as calculating elastic constants (bulk modulus, shear modulus, etc.), powder diffraction patterns, glass transition temperatures (Tg), diffusion constants, pKa values, melting points, water adsorption and various solubilities. For biologics our toolset supports homology modeling, and the calculation of aggregation propensity, titration curves, isoelectric points and viscosity among other things.  Complex and evolving structures, often in fluid states, play a crucial role in the pharmaceutical industry.   For both small-molecule and biologics formulations powerful simulation tools employing atomistic or coarse-grained models to permit the characterization of molecular interactions and nanoscale structuring, sometimes within otherwise disordered bulk systems (e.g., LNP formation, self-assembly of polymer-based structures, dissolving amorphous solid dispersions, liposomes and protein-excipient interactions).

2026 Annual Spring Meeting of the Polymer Society of Korea

Conference

2026 Annual Spring Meeting of the Polymer Society of Korea

CalendarDate & Time
  • April 8th-10th, 2026
LocationLocation
  • Daejeon, Korea

Schrödinger is excited to be participating in the 2026 Annual Spring Meeting of the Polymer Society of Korea conference taking place on April 8th – 10th in Daejeon, Korea. Join us for a presentation by Shaun Kwak, Senior Director of Materials Science Applications Science at Schrödinger, titled “Digital chemistry calling for a paradigm shift in polymer materials innovation.” Stop by booth #33 to speak with Schrödinger scientists.

icon time APR 9 | 16:45
Digital chemistry calling for a paradigm shift in polymer materials innovation

Speaker:
Shaun Kwak, Senior Director of Materials Science Applications Science, Schrödinger

Abstract:
Recent advances in molecular simulation and machine learning technologies are fundamentally reshaping the framework of the discovery and optimization of polymeric materials, quickly replacing the traditional concept of iterative experimentation guided by chemical intuition. Here, we showcase an advanced digital platform technology that combines physics-based molecular simulations with machine learning algorithms to develop novel polymer materials, effectively navigating the vast macromolecular design spaces. Case studies will include, but are not limited to, designing copolymers for semiconductor packaging, optimizing thermochemistry of acrylate-based coating, and assessment of thermal oxidation in thermoset resins. The work demonstrates a major shift of paradigm in the usage of information technology in materials research with broad implications in product lifecycle management, positioning digital chemistry as a cornerstone of the next-generation polymer industry.