Schrödinger User Group Meeting – Materials Science Japan 2024

User Group Meeting

Schrödinger User Group Meeting – Materials Science Japan 2024

CalendarDate & Time
  • October 3rd, 2024
LocationLocation
  • Tokyo, Japan

本会は、10月3日(木)、会場で開催いたします。

弊社サイエンティストや各製品の開発責任者から、最新機能、応用事例、今後の展望などを、セミナー形式でご紹介いたします。

さらに、パナソニックインダストリー株式会社 松澤 伸行様 による特別招待講演を予定しております。松澤様は、高度専門職 であるエグゼクティブエンジニアとして、各種電子部品用の材料設計・開発を担当されています。特別講演では、シュレーディンガーとのコラボレーションによる新規材料の開発事例をご紹介いただきます。

皆様のご参加を心よりお待ち申し上げております。

各発表のアブストラクトはこちらからご覧いただけます。

icon time 10:00 – 10:05
ご挨拶

icon time 10:05 – 10:45
分子動力学計算・密度汎関数計算と機械学習による電解液分子設計

パナソニックインダストリー株式会社 技術本部 プロセスデバイス革新センター エグゼクティブエンジニア 松澤 伸行様

icon time 10:45 – 11:25
Advancing Materials Science with Schrödinger: Latest Innovations, Future Roadmap, and Emerging Applications for Organic Electronics and Beyond

Mathew D. Halls, Senior Vice President, Materials Science, Schrödinger

icon time 11:25 – 12:05
Accelerating Cosmetics Design through Formulations Modeling at the Molecular Level

Jeffrey Sanders, Product Manager and Scientific Lead of Consumer Goods, Schrödinger

icon time 12:05 – 13:00
ランチ

icon time 13:00 – 13:40
Local Formal Chargeを用いた新規チタン酸窒化物の探索

シュレーディンガー株式会社 シニア サイエンティスト 青木 祐太

icon time 13:40 – 14:20
Combined Physics-Based and Machine Learning Approaches in the Design of Complex Materials

Anand Chandrasekaran, Product Manager Materials Science Informatics, Schrödinger

icon time 14:20 – 15:00
ニューラルネットワークポテンシャル表面上の強化学習による遷移状態

シュレーディンガー株式会社 シニア サイエンティスト 大塚 勇起

icon time 15:00 – 15:40
Automated Identification of Chemical Reaction Products with Nanoreactor

Pavel A. Dub, Senior Principal Scientist, Schrödinger

icon time 15:50 – 16:30
高速分子動力学計算と量子力学計算の組み合わせによるGHz以上の高周波数における誘電率と誘電正接の予測

シュレーディンガー株式会社 マテリアルズサイエンス シニア ディレクター 森里 嗣生

icon time 16:30 – 17:10
Polymers and Formulations in Schrödinger Materials Science Suite: From electronics to pharmaceuticals, new properties bring new applications

Andrea Browning, Director – Polymers and Soft Matter, Schrödinger

icon time 17:10 – 17:50
リチウムイオン電池における負極ー電解質界面の解析

シュレーディンガー株式会社 シニア サイエンティスト 井本 文裕

icon time 17:50 – 17:55
閉会

icon time 18:00 – 20:00
懇親会

※海外からの講演者は英語での発表となります。
※Presentations by Japanese speakers are available in Japanese only.

 

【開催形式と会場】
・現地開催です。オンライン配信はございません。
会場
鉃鋼エグゼクティブラウンジ&カンファレンスルーム
〒100-0005 東京都千代田区丸の内1丁目8-2, 鉃鋼ビルディング 南館, 4階
アクセス

※会場参加の登録受付は9月25日(水)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。
※会場参加者様へは、別途メールにて詳細をご案内いたします。

【参加費】
無料

【お申込み方法】
 ▼参加のお申し込みはこちらから▼
https://form.run/@schrodinger-20241003

所属企業または所属機関のメールアドレスにて、ご登録をお願いします。
所属が明らかでない、また、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のであらかじめご了承ください。参加お⼀⼈様につき⼀登録をお願いします。
同業他社さまには参加をご遠慮頂いております。申し訳ございませんが、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 UGM事務局
E-mail: info-japan@schrodinger.com

HomeCare & Beauty Sustainability Summit 2024

Conference

HomeCare & Beauty Sustainability Summit 2024

CalendarDate & Time
  • September 11th-12th, 2024
  • 17:00 CET
LocationLocation
  • Amsterdam, Netherlands

Schrödinger is excited to be participating in the HomeCare & Beauty Sustainability Summit 2024 conference taking place on September 11th – 12th in Amsterdam, Netherlands. Join us for a presentation by Jeffrey Sanders, Product Manager and Scientific Lead of Consumer Goods at Schrödinger, titled “Beyond AI: Leveraging physics-based modeling and machine learning to develop sustainable personal care products.” Stop by booth R4 to speak with Schrödinger scientists.

icon time SEPT 11 | 17:00 CET
icon location 4 Sustainable HomeCare Products Forum
Beyond AI: Leveraging physics-based modeling and machine learning to develop sustainable personal care products

Speaker:
Jeffrey Sanders, Product Manager and Scientific Lead of Consumer Goods, Schrödinger

Abstract:
The journey to develop and reformulate products to become more sustainable has many challenges. Research and development in these areas often demand substantial time, resources, and new raw materials. To accelerate this process, predictive modeling offers the potential to identify promising ingredients, formulations, and even new packaging materials that meet sustainability requirements. A major obstacle in building and deploying useful models is data sparsity. One promising avenue to explore is multi-scale physics-based simulations, as they do not require large experimental datasets as inputs and can be combined with sparse existing data to generate more robust models. This talk will highlight a case study where physics-based simulation was used to accelerate the development of an eco-friendly personal care formulation, and also how molecular-level simulation can be incorporated into machine learning models when little experimental information is available.

Computationally-Guided Drug Formulation Webinar Series

Webinar

Computationally-Guided Drug Formulation Webinar Series

CalendarDate & Time
  • September 11th, 2024 – May 14th, 2025
LocationLocation
  • Virtual

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

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

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

2024 International Elastomer Conference

Conference

2024 International Elastomer Conference

CalendarDate & Time
  • September 9th-12th, 2024
LocationLocation
  • Pittsburgh, Pennsylvania

Schrödinger is excited to be participating in the 2024 International Elastomer Conference taking place on September 9th – 12th in Pittsburgh, Pennsylvania. Join us for a presentation by Manav Bhati, Senior Scientist II at Schrödinger, titled “Design of Elastomers with Tailored Thermal Properties Using Molecular Modeling and Machine Learning.” Stop by booth 918 to speak with Schrödinger scientists.

icon time Sept 11 | 4:30 PM
icon location Expo Hall C
Design of Elastomers with Tailored Thermal Properties Using Molecular Modeling and Machine Learning

Speaker:
Manav Bhati, Senior Scientist II, Schrödinger

Abstract:
Elastomers are integral to industrial applications because of their useful material properties, such as elasticity, durability, and versatility. Innovating new elastomers can lead to the development of superior products that are more durable and stable. A key thermal property of elastomers is the glass transition temperature (Tg), which indicates the temperature at which an elastomer transitions from a glassy/hard state to a soft/rubbery state. Tg is a critical parameter because it determines an elastomer’s operability and performance at various temperatures. Traditional experimental techniques for determining Tg are time-consuming and expensive, necessitating computational approaches to accelerate the design of new elastomers and minimize the failure rate of resource-intensive experimentation. This study focuses on using machine learning (ML) and classical molecular dynamics (MD) simulations to predict the Tg of elastomers. Using curated datasets of experimental Tg values for homopolymers and copolymers from literature, we developed predictive ML models that can accurately predict Tg for new elastomers that are outside of the dataset used to train the model. We then use these ML models to efficiently screen the design space of elastomers by enumerating a large library of copolymer systems. The Tg of the top-performing elastomers were then validated using MD simulations, which have been shown previously to accurately capture the experimental Tg trends of polymer systems. This computational modeling approach not only accelerates the development of new elastomers, but it also provides insights into the relationship between chemical structure and composition to thermal properties.

MS Reactive Interface Simulator

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries

MS Reactive Interface Simulator

Overview

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

Key Capabilities

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

Related Resources

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

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

Energy Capture and Storage

Documentation & Tutorials

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

Materials Science Documentation

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries.

Related Products

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

Jaguar

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

MS Maestro

Complete modeling environment for your materials discovery

MS Reactivity

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

Desmond

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

MS Transport

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

Broad applications across
materials science research areas

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

Polymeric Materials
Catalysis & Reactivity
Energy Capture & Storage

Publications

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

Materials Science Publication

Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory

Materials Science Publication

Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design

Materials Science Publication

Conformers influence on UV-absorbance of avobenzone

Materials Science Publication

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

Materials Science Publication

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

Materials Science Publication

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

Materials Science Publication

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

Materials Science Publication

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

Materials Science Publication

Towards the 4 V-class n-type organic lithium-ion positive electrode materials: the case of conjugated triflimides and cyanamides

Materials Science Publication

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

Schedule a consultation on Schrödinger’s battery solutions.

Contact us today to explore how you can leverage advanced simulation and AI/ML for battery materials.

Don’t see your areas of interest above? Reach out so we can help.

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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.

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.

Hit Discovery Services 

Hit Discovery Services

Hit Discovery Services

Find more diverse hits, faster

Overview

Enable your drug discovery program with Schrödinger’s unrivaled technologies and deep expertise. We’ll give your hit discovery campaigns the best chances of success by leveraging our team of experts and our most advanced technologies for ultra-large virtual screening and rigorous rescoring at scale.

Propel your discovery program with unrivaled technologies and expertise

Benefit from the full impact of Schrödinger’s hit discovery capabilities

• Our team of experts use extensively validated screening and rescoring workflows that leverage Schrödinger’s latest technologies deployed at scale
• Service includes all computing, licensing, and service hours to perform a cutting-edge hit identification campaign with no upfront licensing or hardware costs

Obtain more and higher quality hits with unique rescoring technologies

• Promising compounds are rescored with unmatched accuracy using ABFEP+ amplified by machine learning
• Accurately identify more diverse and potent hits, requiring fewer compounds to be purchased and assayed

Maximize novelty and diversity by screening billions of compounds

• Screen commercial libraries of >5 billion compounds (or >300M for fragments screens) using both structure- and ligand-based approaches simultaneously to maximize the number of unique hits identified
• Explore the largest commercially available libraries for rapid and reliable procurement, including Enamine REAL and WuXi LabNetwork
• Efficiently screen your proprietary or sculpted libraries to explore alternative chemical spaces

From Feasibility Study to Purchase List

Schrödinger has over 20 years of scientific experience in developing industry-leading virtual screening technologies which are used broadly in pharmaceutical companies worldwide.

Through continuous methodology development effort combined with extensive deployment in active drug discovery programs across diverse targets, our team of computational experts have optimized an advanced virtual screening workflow offered in the Hit Discovery Service.

Scientifically-validated solutions for virtual screening

  1. Efficient Exploration of Chemical Space with Docking and Deep Learning.

    Yang et al. J. Chem. Theory Comput. 2021, 17(11), 7106-7119.

  2. Enhancing Hit Discovery in Virtual Screening through Absolute Protein–Ligand Binding Free-Energy Calculations.

    Chen et al. J. Chem. Inf. Model. 2023, 63, 10, 3171–3185.

  3. Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery.

    Zhang et al. J. Chem. Inf. Model. 2023, 63, 6, 1656–1667.

  4. WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand–Receptor Docking.

    Murphy et al. J. Med. Chem. 2016, 59, 9, 4364–4384.

Software and services to meet your organizational needs

Software Platform

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

Modeling Services

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

Support & Training

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

Release 2024-3

Library Background

Release Notes

Release 2024-3

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

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

Force Field

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

Workflows & Pipelining [KNIME Extensions]

In LiveDesign:

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

Binding Site & Structure Analysis

SiteMap

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

Desmond Molecular Dynamics

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

Mixed Solvent MD (MxMD)

  • Improved organization of output structures and data in prjzip file

Hit Identification & Virtual Screening

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

Ligand Preparation

Hit Analysis

  • Filter chemotypes by SMARTS in Hit Analyzer Panel

FEP+

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

Protein FEP

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

Solubility FEP

  • Expanded OPLS5 support for Solubility FEP simulations

FEP Protocol Builder

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

Biologics Drug Discovery

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

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

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

Transport Calculations via MD simulations

Product: MS Transport

  • Diffusion: Support for non-orthorhombic systems as input

Materials Informatics  

Product: MS Informatics

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

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

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

Reactivity

Product: MS Reactivity

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

Microkinetics

Product: MS Microkinetics

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

MS Maestro Builders and Tools

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

Classical Mechanics

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

Quantum Mechanics

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

Education Content

Life Science

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

Materials Science

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

LiveDesign

What’s new in 2024-3

  • Uploads from Maestro to LiveDesign could fail if the LiveDesign project had more than 32,000 columns, and now complete successfully regardless of the number of columns
  • LiveReports that contained columns with many values would show red error bars at the top of the LiveReport, and now no longer show the red error bars
  • LiveReport tabs would disappear after logging out and logging in, and now correctly appear after logging back in
  • New LiveDesign Learning module for rapid AI/ML molecular property predictions: Enables highly scalable, automated AI/ML pipelines for drug design
    • *LiveDesign Learning is now called LiveDesign ML
  • Accelerated scaffold and R-group design with AutoDesigner Core Design: Automatically generate and optimize novel cores and R-group(s) simultaneously
  • Delete Published Freeform column and Formula columns from the Data & Columns Tree
  • Biologics:
    • Sequence-activity relationships in the Sequence Viewer:
      • Ability to add a quantitative column from the LR in the viewer
      • Correlate the changes in the residues and the activity data with the heatmap
    • There would only be one option when trying to import the Biologics data via csv and the option “Import As Single Entity for CSV” won’t show now.
    • Performance of structure hierarchy loading and item selection through hierarchy panel in the 3D Visualizer are improved.
    • Double-clicking an item in the hierarchy zooms to that selection in the 3D Visualizer workspace.
    • Set gap penalties in the sequence viewer to generate more useful alignments
  • Landing Pages:
    • The Landing page now links to a specific URL and enable bookmarking the Landing Page in a browser
    • Download resources and files from the Landing Page Resource page
  • Spreadsheet View:
    • A warning message alerting the user to expect decreased performance now appears on LiveReports that contain more than one million cells
    • Entity images no longer enlarge when hovering over the image, and can now be zoomed by clicking a magnifying glass button that appears to the right of the entity image
  • The User details page in the Admin Panel now shows a warning that unlicensed usernames will not appear in dropdown lists throughout LiveDesign
  • Models now support date and datetime returns
  • Forms Matrix Widget now render larger editing areas for Freeform column cells when the cells are small

What’s Been Fixed

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

Training & Resources

Online Certification Courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

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NAMES 2024

Conference

NAMES 2024

CalendarDate & Time
  • August 8th-9th, 2024
LocationLocation
  • Ann Arbor, Michigan

Schrödinger is excited to be participating in the NAMES 2024 conference taking place on August 8th – 9th in Ann Arbor, Michigan. Join us for a workshop by Katie Dahlquist, Senior Scientist at Schrödinger, titled “Empowering Exploration: A Workshop on Molecular Modeling for Materials Science.”

Speaker:
Katie Dahlquist, Senior Scientist, Schrödinger

Date/Time:
Friday, August 9 | 1:30PM – 3:30PM

Abstract:
The Schrödinger Materials Science platform is a single interface with access to structure building, simulation, and analysis for atomic-scale simulation. With respect to simulation, the platform has extensive capabilities in molecular and periodic quantum mechanics (namely density functional theory calculations), molecular dynamics, and machine learning. This workshop will guide participants to work hands-on with the Schrödinger Materials Science platform. We will instruct attendees through parts of two of our online courses which are best-suited for the NAMES audience: Polymeric Materials and Battery Materials.

Empowering scientists with integrated AI/ML modeling for rapid molecular property predictions

AUG 13, 2024

Empowering scientists with integrated AI/ML modeling for rapid molecular property predictions

AI/ML models are powerful tools for predicting diverse physical and chemical properties of small molecules. However, fine-tuning these models is resource-intensive and challenging to scale for numerous, frequently updated datasets. Automating this process, and ensuring models are re-trained as new data becomes available, enhances the efficiency of using AI/ML models to advance drug discovery programs.

In this webinar, we will present LiveDesign ML, a new module in Schrödinger’s LiveDesign collaborative enterprise informatics platform, for training and deploying state-of-the-art AI/ML models with minimal manual intervention. LiveDesign ML treats datasets as dynamic information feeds that evolve as scientists explore new chemistry to deliver optimized AI/ML models. It provides dynamic, reliable, and rapid molecular property predictions in an interactive design environment, allowing teams to triage newly sketched design ideas or hundreds of thousands of compound ideas in minutes for large library screening.

We will demonstrate use cases of LiveDesign ML through several recent case studies from Schrödinger’s Therapeutics Group where the technology has allowed teams to overcome critical design challenges and advance programs.

Highlights

  • Overview of LiveDesign ML features and user interface
  • Demonstration of LiveDesign ML for AI/ML molecular property predictions using experimental and/or in silico data
  • Ability to triage hundreds of thousands of compound ideas in minutes for large library screening
  • Success stories within Schrödinger’s drug discovery projects

Our Speakers

Jennifer Knight

Director, Schrödinger

Jen Knight is a Director in the Schrödinger Therapeutics Group. She has been at Schrödinger since 2012 and has been a modeling lead on internal projects and collaborations. She specializes in free-energy methods, LiveDesign workflow optimization and machine learning applications.

Zach Kaplan

Senior Principal Scientist, Schrödinger

Zach Kaplan is a senior principal scientist on Schrödinger’s machine learning team. Since 2019, Zach has contributed to the research, development, and application of Schrödinger’s ML tools. He leads the ML Med Chem applications team and is the product manager of Schrödinger’s DeepAutoQSAR and LiveDesign ML. Prior to joining Schrödinger, Zach studied applied mathematics at Brown University.

Designing better packaging materials with a reduced risk of contamination and longer shelf-life using molecular simulations 

Designing better packaging materials with a reduced risk of contamination and longer shelf-life using molecular simulations

Molecular dynamics simulation of plastic contaminant migration in packaging materials and potential leaching into model food fluids

 Executive Summary

  • Built and validated a molecular model that can predict bulk and interfacial penetrant diffusion, as well as enable an understanding of the underlying mechanisms governing these processes
  • Established a modeling procedure to successfully carry out the challenging simulations of migration processes within and from polymer phases
  • Gained valuable insights to complement and rationalize labor- and time-intensive penetrant migration experiments for product developers, regulatory agencies, and manufacturers
Examples of bulk and interfacial structures employed in the molecular modeling
of penetrant diffusion in polymeric systems.

Approach

In Mileo et al., Schrödinger scientists employed molecular dynamics (MD) simulation using the Schrödinger Materials Science Suite, Desmond MD engine and the OPLS4 force field. The goal of this work was to analyze the transport of monomers in three commercially important, recyclable polymers: polyamide-6 (PA 6), polycarbonate (PC), and poly(methyl-methacrylate) (PMMA). To achieve this, scientists performed the following steps:

  1. Validated bulk polymeric models with respect to properties derived from experimental work 
  2. Verified the predictability of the modeling strategy in reproducing the experimental monomer migration tendencies by employing different solvents to simulate foodstuff
  3. Predicted the monomer migration mechanism in two typical components employed in the food industry (palmitic acid and capric triglyceride)

Conclusion

This work demonstrates how molecular-scale insights can aid the design of safe and functional polymer/formulation interfaces in industry-relevant consumer goods. The methods presented can also be leveraged to understand the risk of contaminants leaching into food or other consumer products, alongside understanding how the product itself can impact the rate of contamination at a barrier interface.

Snapshot obtained from MD simulation displaying the imminent migration of a monomer (methyl methacrylate, in green) from its polymer matrix (polymethyl methacrylate, in purple) towards a palmitic acid formulation.

Publications

  1. Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids

    Mileo PG, et al. Langmuir 2024, 40, 24, 12475–12487

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.

Modeling 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.

EFMC International Symposium on Medicinal Chemistry

Conference

EFMC International Symposium on Medicinal Chemistry

CalendarDate & Time
  • September 1st-5th, 2024
LocationLocation
  • Rome, Italy

Schrödinger is excited to be participating in the EFMC International Symposium on Medicinal Chemistry taking place on September 1st – 5th in Rome, Italy. Join us for a presentation and workshop by Schrödinger scientists. Stop by booth #60 to speak with Schrödinger scientists.

icon time Monday | 12:30 – 1:15 PM
icon location Room Quirinale
Workshop: Prioritizing DLK Inhibitors for Potency, Selectivity, and Brain-penetration: a Digital Chemistry Design Challenge

Speakers:
Guillaume Paillard, Lead Customer Success Manager, Schrödinger
Jonas Kaindl, Senior Scientist II, Schrödinger

Abstract: In this hands-on workshop, we will use Schrödinger’s LiveDesign platform to design and triage DLK inhibitors using a series of predictive models. We will highlight how LiveDesign can be used to identify and address program challenges as well as predict the various different endpoints to allow for informed synthesis decisions. The workshop will feature the following capabilities:
– Interactive 2D/3D design with Ligand Designer
– Substructure filtering and structurally-aware formulas for labeling subseries
– Use of forms view and plotting to identify correlations between calculated and experimental data points
– Integration of advanced computational methods like E-sol for predicting Kpu,u and FEP+ for predicting binding affinity
– Development of MPO scores for prioritizing synthesis decisions
The workshop will be concluded with a design challenge that is aimed to identify selective and potent inhibitors that match the developed MPO.

icon time Wednesday | 11:45 – 12:05 PM
icon location Auditorium Capitalis
Accelerated In Silico Discovery of SGR-1505: a Potent Malt1 Allosteric Inhibitor for the Treatment of Mature B-cell Malignancies (LE063)

Speaker:
Dr. Michael Trzoss, Principal Scientist, Schrödinger

Abstract: MALT1 (Mucosa-associated lymphoid tissue lymphoma translocation protein 1) is a component of the MALT1-BCL10-CARD11 complex downstream from the Bruton Tyrosine Kinase (BTK) on the B-cell receptor signaling pathway. MALT1 is a key mediator of nuclear factor kappa B (NF-κB) signaling, which is the main driver of a subset of B-cell lymphomas. MALT1 is considered a potential therapeutic target for several subtypes of non-Hodgkin’s B-cell lymphomas and chronic lymphocytic leukemia (CLL), including tumors with acquired BTK inhibitor (BTKi) resistance. Constitutive activation of the NF-κB is a molecular hallmark of activated B cell-like diffuse large B cell lymphoma (ABC-DLBCL), and MALT1 may have utility as a treatment option for ABC-DLBCL. Furthermore, a third-party MALT1 inhibitor recently showed strong anti-tumor activity in mature B cell malignancies from Phase 1 studies.

By applying advanced physics-based modeling techniques, including combining free energy calculations with machine learning methods and chemistry-aware compound enumeration workflow, the team explored extensive sets of de novo design ideas to quickly identify a novel hit series with an in vivo tool molecule to establish an in vivo PD and efficacy mouse model early on in the project. Multi-parameter optimization (MPO) allowed efficient prioritization of molecules with good potency and drug-like properties during lead optimization. This led to the discovery of a highly potent MALT1 inhibitor, SGR-1505, with a well-balanced property profile in under a year, with only 78 compounds synthesized in the lead series and 129 compounds overall. SGR-1505 is a potent and orally available allosteric MALT1 inhibitor. It demonstrated strong anti-tumor activity alone and in combination with BTK inhibitors in multiple in vivo B-cell lymphoma xenograft models. Currently, a Phase 1 clinical trial with SGR-1505 in patients with mature B-cell neoplasms is ongoing (NCT05544019).

MS Microkinetics

MS Microkinetics

Efficient tool for surface reaction kinetics

MS Microkinetics

Overview

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

Key Capabilities

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

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

Efficient tools and solutions to predict activation energies

Quantum ESPRESSO GUI

Integrated graphical user interface for nanoscale quantum mechanical simulations

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MS Reactivity

Automatic workflows for accurate prediction of reactivity and catalysis

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AutoTS

Automatic workflow for locating transition states for elementary reactions

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Accelerate the design of high-performance heterogeneous catalysts

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

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Complete modeling environment for your materials discovery

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Documentation & Tutorials

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

Materials Science Documentation

MS Microkinetics

An efficient tool for surface reaction kinetics.

Materials Science Documentation

Materials Science Panel Explorer

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

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.