Date & Time 
Location 
Schrödinger is excited to be participating in the GRC Computational Materials Science and Engineering conference taking place on July 21st – 26th in Newry, Maine. Join us for a poster by Atif Afzal, Principal Scientist at Schrödinger, titled “Advancements in Polymer Electrolyte Dynamics through Machine Learning-Based Force Fields.”
Date & Time Location
Schrödinger is excited to be participating in the Biennial Conference on Chemical Education 2024 taking place on July 28th – August 1st in Lexington, Kentucky. Join us for a workshop by Rachel Clune, Senior Scientist I at Schrödinger, titled “Online certification courses to help experimental graduate students incorporate molecular modeling into their research.” Stop by booth 43 to speak with Schrödinger scientists.
Speaker:
Rachel Clune, Senior Scientist I, Schrödinger
Abstract:
Schrödinger’s materials science online certification courses teach students about applications of molecular modeling in chemistry and materials science through the use of the Schrödinger Materials Science platform. The target audience of the courses are experimental scientists and engineers wishing to broaden the tools they have at their disposal to understand and advance their research. The courses come with access to computing resources, allowing for researchers without access to computing clusters to still perform complex calculations. This makes the courses particularly useful for graduate students whose main focus is in experimental domains but want to make use of computational tools to further improve their research.
For the workshop, participants will be given a limited license to the Schrödinger Materials Science platform and access to a virtual cluster to run calculations. The courses are guided by active learning principles, with tutorials on how to set up and run different calculations interspersed with short (~10 minute) lectures. We will guide participants through one of the materials science online certification courses for the first 1.5-2 hours of the workshop with periodic breaks for discussion and to make sure participants are staying on track. Examples of the types of tools that will be discussed include density functional theory calculations, all-atom molecular dynamics simulations, and the use of machine learning for predicting material properties. The last hour will be reserved for participants to attempt their own calculations with guidance and help from the workshop presenters. By the end of the workshop, participants should feel comfortable using the Schrödinger’s Materials Science platform and have an understanding of how computational modeling can be an asset to their research goals.
Participants will need to bring their own laptop that is able to connect to the venue wifi and the workshop presenters will need access to a projector.
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 to improve drug formulations and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.
Schrödinger offers a range of computational solutions for advancing pharmaceutical formulation, from crystalline or amorphous form characterization to selection of materials and excipients for processing, formulation, and delivery.
De-risk your solid form selection process by identifying the most stable polymorph at room temperature
Overcome the risks associated with disappearing polymorphs in late stage drug development. For a given active pharmaceutical ingredient (API), we will leverage our proprietary crystal structure prediction (CSP) platform to identify the most stable crystal polymorph at room temperature. Starting from a 2D structure of the API, we deliver to you the thermodynamic stability ranking of crystal polymorphs.
Discover how Schrödinger technology is being used to solve real-world research challenges.
Level-up your skills by enrolling in our online course, Molecular Modeling for Materials Science: Pharmaceutical Formulations.
Learn MoreGet answers to common questions and learn best practices for using Schrödinger’s software.
Learn more about the key computational technologies available to progress your research projects.
Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties
Secure, scalable environment for running simulations on the cloud
High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy
Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales
Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties
De-risk your solid form selection process by identifying the most stable polymorph at room temperature
Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.
Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecular modeling, design, and collaboration.
Leverage Schrödinger’s computational expertise and technology at scale to advance your projects through key stages in the drug discovery process.
Access expert support, educational materials, and training resources designed for both novice and experienced users.
Date & Time Location
Schrödinger is excited to be participating in the 38th ACS National Medicinal Chemistry Symposium taking place on June 23rd – 26th in Seattle, Washington. Join us for a presentation and workshop by Jennifer Knight, Director at Schrödinger.
Driving Innovation with Machine Learning: Impact on a Pipeline of Drug Discovery Programs (IL27)
Speaker: Dr. Jennifer Knight, Director
Abstract:
Physics-based modeling and machine learning approaches are used widely in our drug discovery programs and collaborations to design new compounds and to model potency and ADMET properties. Here, we present several case studies of machine learning strategies employed in our active drug discovery programs including: using active learning with free energy predictions to efficiently profile large chemical spaces, leveraging experimental data for enhancing ADMET profiles in lead optimization using an interactive ML dashboard and applying de novo design workflows for intelligent molecular core design.
Prioritizing DLK inhibitors for potency, selectivity, and brain-penetration: A digital chemistry design challenge
Host: Wade Miller, Senior Manager
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:
The DLK program was driven by the models listed above, as well as FEP+ models for both on-target and off-target potency.
The participant who has the best design, as determined by the project MPO, will receive a free seat to one of Schrödinger Online Certification Courses.
Director, Schrödinger
Dr. Jennifer Knight is a Director at Schrödinger, based in New York City. She received her Ph.D. in Chemistry & Chemical Biology with Ron Levy from Rutgers University and undertook postdoctoral training with Charles Brooks III at The Scripps Research Institute and the University of Michigan. Dr Knight joined the Scientific Development team at Schrödinger in 2012 and for the past seven years has been a member of the Schrödinger Therapeutics Group. She spearheads modeling teams employing the full spectrum of in silico strategies, including free energy calculations and machine learning approaches. She is a champion for workflow optimization and their implementation at-scale to help drive drug discovery projects forward.
Senior Manager, Schrödinger
Wade Miller is a Senior Manager on the Schrödinger Education team. He received his BA in Chemistry from the University of Pennsylvania, where he performed research on the history and philosophy of chemistry. Since joining Schrödinger, Wade was involved in creating the Introduction to Molecular Modeling in Drug Discovery course and led the creation of the Introduction to Computational Antibody Engineering course. He has run over 100 workshops on molecular modeling for academic and industry audiences.
Date & Time Location
Schrödinger is excited to be participating in the CRS 2024 Annual Meeting & Exposition conference taking place on July 8th – 12th in Bologna, Italy. Join us for a presentation by Irene Bechis, PhD, Senior Scientist at Schrödinger, and Dan Cannon, PhD, Principal Scientist at Schrödinger, titled “Molecular modeling and machine learning for small molecule and biologic drug formulation.”
2:30 PM – 3:30 PM CET Given the competitive market and inherent challenges in drug formulation, selecting and combining the right ingredients in the appropriate manner is essential. With advances in machine learning, physics-based simulation and compute hardware, modeling is emerging as a valuable source of information and knowledge to complement experimental characterization.
In this talk, we showcase several case studies illustrating how the tools from the Schrödinger platform can be applied to modeling formulations of small molecule and biologic drugs. Starting from data-driven approaches, we demonstrate how our machine-learning tools can provide an efficient prediction of drug solubility and other important properties of multi-component mixtures. We then describe physics-based approaches to study those complex and evolving structures, often in fluid states, that play a crucial role in the pharmaceutical industry.
For both small molecule and biologics formulations, we have developed powerful simulation tools employing atomistic or coarse-grained models to enable the characterization of molecular interactions and nanoscale structuring. For example, we can address the dissolution of amorphous solid dispersions, the self-assembly of polymer-based structures and the viscosity and aggregation of protein-excipient mixtures. We also present recent work on simulating the self-assembly and calculating the apparent pKa values of lipid nanoparticles used in the delivery of mRNA.
Senior Scientist, Schrödinger
Principal Scientist, Schrödinger
Date & Time Location
Schrödinger is excited to be participating in the TechConnect World Innovation conference taking place on June 17th – 19th in Washington, DC. Join us for a presentation by Alex Chew, Principal Scientist at Schrödinger, titled “Leveraging Physics-Based Simulations and Machine Learning to Identify Promising Formulations for Materials Science Applications.” Stop by booth #730 to speak with Schrödinger scientists.
1:30 PM 
Session AI Modeling & Simulation Formulations – or a mixture of chemical ingredients – are ubiquitously found across material science applications, such as copolymer blends, consumer packaged goods, and energy storage devices. These mixtures consist of multiple chemical species with known compositional information, but their bulk properties are challenging to predict because they emerge from non-obvious intermolecular interactions arising between multiple species that heavily depend on both molecular structure and composition. Trial-and-error experimentation to optimize these formulations is cost-prohibitive because of the large chemical design space that is exacerbated by the tunability of their compositions. Computational approaches that could traverse the expansive design space offer a promising alternative solution to finding better formulations. Physics-based approaches, such as classical molecular dynamics simulations (MD), could accurately predict formulation properties by accounting for all possible interactions between multiple molecules. However, rapid screening with MD remains challenging due to its computational cost, which motivates the use of data-driven approaches to more efficiently screen the formulation design space. Given the lack of publicly available datasets, we generate a large formulation dataset from physics-based simulations consisting of more than 30,000 solvent mixtures that were selected based on experimental solubility tables. We then benchmark descriptor-based and graph-based molecular representations, as well as a variety of machine learning architectures, to identify accurate formulation-property relationships that could predict formulation properties given individual molecular structures and compositions as input. Given the large design space of chemistries and compositions, we leverage an active learning framework to iteratively suggest the next best compounds or compositions to test starting with a small dataset (~100 examples). Leveraging physics-based simulations to curate a formulation dataset and the development of accurate formulation-property relationships enables us to rapidly identify promising formulations for a wide range of materials applications, such as liquid electrolytes for batteries, copolymers for surface coating, solvent additives for perfumes or paints, and more.
Date & Time Location
本会は、18日(金)、会場で開催します。
新規テクノロジーの開発状況を始め、実際の創薬プロジェクトでの活用事例を、国内外のユーザー様からのご発表を含めてご紹介いたします。
多くの皆様のご参加を心よりお待ちしております。
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。
10:00AM – 10:10AM
10:10AM – 11:10AM Aleksey Gerasyuto, Vice President, Drug Discovery, Head of Chemistry, Schrödinger
11:15AM – 0:00PM アステラス製薬株式会社 開発研究部門 ディスカバリーインテリジェンス 主管研究員 天野 靖士様
0:00PM – 1:00PM
1:00PM – 1:45PM Andrew Placzek, Principal Scientist, Schrödinger
1:50PM – 2:35PM Matt Repasky, Senior Vice President, Schrödinger
2:40PM – 3:25PM Hideyuki Igawa, Director, Therapeutic Group, Schrödinger
3:25PM – 3:50PM
3:50PM – 4:35PM David J. Huggins, Executive Director, Computational Biomedicine Sanders Tri-Institutional Therapeutics Discovery Institute, Bronk Laboratory
4:40PM – 5:25PM 武田薬品工業株式会社、リサーチ ニューロサイエンス創薬ユニット NCEプロダクション研究所 髙木 輝文様
5:25PM – 5:35PM
5:45PM – 7:45PM
※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
18日 ドラッグディスカバリー サミット
〒104-0031 東京都中央区京橋2-1-3 京橋トラストタワー4階 トラストシティ カンファレンス・京橋
※会場参加の登録受付は7月10日(水)23:59までといたします。
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※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-202407DDS
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※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
E-mail: info-japan@schrodinger.com
Date & Time Location
Schrödinger is excited to be participating in The Battery Show Europe conference taking place on June 18th – 20th in Stuttgart, Germany. Join us for a presentation by Leonie Koch, Principal Application Scientist at Schrödinger, titled “A digital chemistry strategy to accelerate the design of novel battery technologies.”
10:30 AM – 10:45 AM
Open Tech Forum Leonie Koch, Principal Application Scientist, Schrödinger
Abstract:
The battery market has become a critical economic sector within the automotive industry, demanding for improved, reliable, and lower cost solutions. Physics-based modeling and machine learning can significantly accelerate electrolyte selection and characterization, ensuring that target properties are met. In this presentation, we will show how Schrödinger’s digital chemistry technology can catalyze selection processes by predicting key properties of battery electrolytes, using physically relevant descriptors, and the application of machine learned force fields to overcome the current limitations of ab-initio density functional theory calculations for getting insights into the nucleation and dynamic evolution of the complex solid electrolyte interphase (SEI) in next-generation battery technologies.
A powerful molecular visualization tool offered free to academia
Maestro Viewer is an intuitive interface for academic users to visualize and manipulate 3D structures using Schrödinger’s powerful rendering capabilities and chemical building tools. Maestro Viewer provides academic users the opportunity to gain familiarity with the Maestro interface and its visualization capabilities.
Maestro Viewer does not include access to molecular property predictions or the ability to run molecular simulations and is only available with an academic account.
Maestro is a powerful molecular modeling environment for accessing cutting-edge physics-based molecular simulation workflows, state-of-the-art machine learning, and advanced structure visualization.
Expand your access with large scale, university-wide access to Schrödinger software. Perform cutting edge research and train the next generation of scientists at your university with Schrödinger’s most comprehensive set of large-scale software licenses, spanning life science and materials science applications.
Date & Time Location
半導体や電子部品から日用品に至るまで、あらゆる材料開発において、計算化学は近年では欠かせない基盤技術の一つとしてその存在を確立しつつあります。
シュレーディンガーは、1990年の会社創立以来、分子設計向けソフトウェアとインフォマティクスの機能強化に継続的に取り組み、様々な材料開発の効率化への実用的なソリューションとして提供しています。
この度、これらのソフトウェアを活用した事例をご紹介するセミナーを開催します。
シミュレーションや機械学習のご経験がある方はもちろん、これから手掛けるという方にもご満足いただける内容になっております。
ご興味のあるセッションのみの聴講も大歓迎ですので、ぜひお気軽にご参加ください。
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。
⽇本時間10:00AM – 11:00AM Mohammad Atif Faiz Afzal, Ph.D., Principal Scientist, Schrödinger
⽇本時間11:00AM – 12:00AM 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（博士（理学））
⽇本時間10:00AM – 11:00AM Haidong Liu, Ph.D., Senior Scientist II, Schrödinger
⽇本時間11:00AM – 12:00AM Pavel A. DUB, Senior Principal Scientist, Schrödinger
⽇本時間5:00PM – 6:00PM Simon D. Elliott, Ph.D., Director – Atomic level process simulation, Schrödinger
⽇本時間10:00AM – 11:00AM Garvit Agarwal, Ph.D., Scientific Lead, Energy Storage, Schrödinger
⽇本時間11:00AM – 12:00AM 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（博士（工学））
⽇本時間10:00AM – 11:00AM シュレーディンガー株式会社 ストラテジック デプロイメント マネージャー 石崎 貴志（博士（理学））・シニア ソリューション アーキテクト 山田 淳美
⽇本時間11:00AM – 12:00AM 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（博士（工学））
【セミナー形式】
Zoom webinarを使⽤したオンライン形式
※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.
【お申込み⽅法】
▼参加のお申し込みはこちらから▼
https://schrodinger.zoom.us/webinar/register/WN_dbPy5O52RASAIqAp1xfJcQ#/registration
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。アクセスリンクの共有はご遠慮ください。
当⽇は、お申し込みの際に登録いただいた⽒名·メールアドレスにてご参加ください。
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
E-mail: info-japan@schrodinger.com
Date & Time Location
Schrödinger is excited to be participating in the DDF Summit taking place on May 21st – 23rd 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.”
Fellow
John earned a MSc from the University of Waterloo in theoretical chemistry and a PhD from the University of Pennsylvania in computational chemistry. Following post-doctoral research in computational chemistry at the University of British Columbia, he worked for Procter & Gamble studying surfactant structures in solution. For the last 23 years, John has worked for Schrödinger, LLC, as a scientific software developer and a research scientist, managing a number of products including the Materials Science Coarse-Grained product. John has focused on computer modeling of drug formulations for much of the last 8 years.
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).
Date & Time Location
Schrödinger is excited to be participating in the SID Display Week conference taking place on May 12th – 17th in San Jose, California. Stop by booth 1634 to speak with Schrödinger scientists.
Join us for a free workshop day on May 15th in Meeting Room 213. Schrödinger experts will walk you through guided demos and help you gain hands-on experience using digital simulations to expedite your organic electronics R&D. Register for the workshop here.
May 16 | 11:40 AM – 12:10 PM
Exhibit Hall Center Stage Speaker: Hadi Abroshan, Principal Scientist, Schrödinger
May 17 | 10:00 AM – 10:20 AM
San Jose Convention Center, LL21CD Speaker: Hadi Abroshan, Principal Scientist, Schrödinger
Abstract: Empowered by digital chemistry and informatics platforms, this study highlights the transformative impact of physics-based simulation, machine learning, and data management in display industry R&D. This technology integration accelerates ideation and decision-making, ensuring swift, accurate, and cost-effective development for next-generation display devices.
