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Schrödinger is excited to be participating in the 2026 CMC Conference conference taking place on April 14th – 16th in Portland, Oregon. Join us for a presentation by Simon Elliott, Director, Atomic Level Process Simulation at Schrödinger, titled “Winding Road Ahead: Challenges for the Semiconductor Industry in AI-Enabled Materials Discovery.”
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Schrödinger is excited to be participating in the Peter O. Stahl Advanced Design Forum taking place on May 14th – 15th in Wayzata, Minnesota. Join us for a presentation by Anand Chandrasekaran, Product Manager, Materials Science Informatics at Schrödinger, titled “Generative AI for Materials.”
MAY 14 | 1:30 PM Speaker:
Anand Chandrasekaran, Product Manager, Materials Science Informatics at Schrödinger
Date & Time Location
Schrödinger is excited to be participating in the ACS Spring 2026 conference taking place on March 22nd – 26th in Atlanta, Georgia. Join us for presentations by Atif Afzal, Principal Scientist II, Materials Science Modeling Services at Schrödinger. Stop by our booth to speak with Schrödinger scientists.
MAR 23 | 8:45 AM 
Room C207 Speaker: Atif Afzal, Principal Scientist II, Materials Science Modeling Services, Schrödinger
Division: I&EC: Division of Industrial and Engineering Chemistry
Session: Data Analytics and AI For Chemistry, Manufacturing, and Healthcare
MAR 23 | 2:55 PM
Room B401 Speaker: Atif Afzal, Principal Scientist II, Materials Science Modeling Services, Schrödinger
Division: COMSCI: Committee on Science
Session: AI for Chemistry: From Algorithms to Applications
Date & Time Location
Schrödinger is excited to be participating in the 38th Molecular Modeling Workshop 2026 conference taking place on March 9th – 10th in Erlangen, Germany. Join us for a presentation and workshop by Jonas Kaindl, Principal Scientist I at Schrödinger.
Speaker: Jonas Kaindl, Principal Scientist I at Schrödinger
Abstract: If you’re interested in running molecular dynamics simulations in your research, in this workshop you can learn how to run MD simulations with the Schrödinger suite. We’ll use two examples, a small molecule ligand in water and a protein-ligand complex, and look at how to prepare the structures and set up the simulation. Once the simulation is completed, we’ll go through different methods to analyze trajectories in Maestro, including analyzing RMSDs, occupations, solution phase conformers, solvation patterns and other interesting properties.
Preparation:
Every participant is required to download the Schrödinger license and bring their own laptop with the Schrödinger suite installed. Since we will not go through the basic functionalities of Maestro, we strongly encourage participants to go through our “Getting going with Maestro” section on our website to learn the basics (requires a free website account): Getting going with Maestro
Should you wish to prepare further, here are additional resources:
Learning Paths for Small Molecule Drug Discovery
Introduction to Structure Preparation and Visualization
Speaker: Jonas Kaindl, Principal Scientist I at Schrödinger
Abstract: The estimation of drug-target residence time has been widely adopted in drug discovery and lead optimization campaigns as a metric to control and modulate in vivo drug efficacy. Over the years, several computational approaches have been developed to simulate unbinding kinetics and calculate dissociation rates. In addition to accurately predicting residence time, understanding the molecular basis of the unbinding event is crucial to support and drive the design of drugs with optimized kinetic profiles. Here, we present the application of the unbinding kinetics workflow developed by Schrödinger to accurately predict the residence time and to study the unbinding mechanism of a set of drug-target systems [1]. We applied the presented approach to different target classes and modalities, looking at the details of the dissociation process and understanding the determinants of such an event. Overall, the results demonstrate the applicability of the workflow in assisting drug design with minimal human intervention and a computational cost compatible with drug design cycle timeline.
次世代半導体デバイスでは、低誘電率や低エッチングレートを実現する新しい誘電材料の開発が求められています。その有力な手法の一つが、シリコンナイトライドへの炭素導入を可能にする原子層堆積（ALD）プロセスです。
本ウェビナーでは、SchrödingerとLam Researchのコラボレーション事例を通じて、計算科学(DFT)と実験(RGA、FTIR)を組み合わせ、最適な前駆体を効率的に選定するアプローチをご紹介します。
理論と実験を連携させることで、プロセス理解を深め、材料開発を加速する具体的な方法を分かりやすく解説します。
こんな方におすすめです
Director of Atomic Level Process Simulation, Schrödinger
材料の成膜およびエッチングにおける表面化学へ原子スケールモデルを応用する分野の第一人者です。2023年にはALD Innovator Awardを受賞しています。
AI and machine learning (ML) are often sold as push-button solutions for materials design and discovery, but they lack value without a rigorous foundation. While the current AI revolution provides unprecedented speed and possibilities, human know-how is a key ingredient for ensuring complex methods lead to high impact outcomes. True innovation happens at the intersection of physics-based simulation, AI/ML, and human expertise. Join us to explore how Schrödinger’s domain experts integrate these three pillars to streamline material optimization.
We’ll discuss how to move beyond the hype and apply digital chemistry strategies that deliver meaningful business results. We will introduce Schrödinger’s Materials Science platform, and share high-impact case studies from a variety of industries and applications, ranging from small molecules to formulations and electronics to industrials. Recent advancements, such as device level ML and cutting-edge machine learning force field (MLFF) architectures will be presented.
Director of Materials Science, Schrödinger
Michael Rauch is a Director at Schrödinger specializing in materials science and education. Michael earned his Ph.D. from Columbia University in synthetic organometallic chemistry as an NSF Graduate Research Fellow before pursuing a postdoctoral role in organic chemistry at the Weizmann Institute of Science as a Zuckerman Postdoctoral Scholar. Michael is particularly interested in green, sustainable chemistry and transforming the way that synthetic chemists utilize molecular modeling via practical education.
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Location
Dear Medicinal Chemists,
Ever found yourself struggling to predict brain permeation (Kp,uu), efflux, hERG inhibition, or CYP3A4 TDI in your projects? Take a break from your other obligations and let’s talk!
We are inviting you to join us in an interactive and free-of-charge session on Thursday, March 26th at the Radisson Collection Hotel, Grand Place Brussels for an extended version of our Lunch and Learn series where Schrödinger scientists will be diving deep into these crucial areas, bringing your practical solutions and expert insights, and demonstrating how modeling approaches can significantly help medicinal chemists make their projects more efficient and successful. You can either join for the whole event or solely for the presentation session.
Thursday, March 26th, 2026
From 10:00 to 14:00 CET
+ Welcome Coffee
09:30 – 10:00
Interactive Session on Advanced Modeling Approaches in Medicinal Chemistry
10:00 – 12:30
David Papin, Principal Scientist II, Applications Science
Zeineb Si Chaib, Principal Scientist II, Applications Science
Our seminar will use the DLK story as a framework to explore the topics in detail throughout our session. Afterwards, lunch is served.
+ Lunch
12:30 – 14:00
Discussion & Networking
14:00 – open end
Join us in the afternoon for a Q&A and networking session with our Scientists and Account Managers, providing an opportunity to present your questions and challenges, which the Schrödinger team will endeavor to address.
Register today to secure your seat!
The seminar is free to attend but preregistration is required as seats are limited. Previous-experience with the Schrödinger suite is not required.
Principal Scientist, Schrödinger
Senior Scientist II, Applications Science, Schrödinger
Date & Time Location
Schrödinger is excited to be participating in the Future Food Tech 2026 conference taking place on March 19th – 20th in San Francisco, California. Join us for a panel discussion, titled “AI Innovation Lab“. Stop by our booth to speak with Schrödinger scientists.
MAR 20 | 13:30 Speakers:
Federico Fontanella, Head of Strategic
Innovation and Product Partnerships, Trace One
Jeff Sanders, Product Manager, Schrödinger
Jay Gilbert, Director, Co-Developer & Digital Products, IFT
Eric Hamborg, Chief Commercial Officer – PIPA
Elizabeth Crawford, Senior Editor – Food Navigator USA
Abstract:
AI is rapidly transforming how food companies innovate, operate, and compete – how are you putting it to work?
This immersive session shows how AI is driving real impact across the food system, from faster product development to smarter operations.
Collaborate with forward-thinking peers through:
• Unpacking real-world AI success stories across product development, operations, and supply chains
• Live AI product demos showcasing tools accelerating formulation, insight generation, and decision-making
• Campfire-style breakout sessions where groups develop and present practical AI use cases, including benefits and risks.
Whether you’re starting out or scaling, this session will give you practical insights and actionable AI ideas to take back to your business.
従来のメディシナルケミストリーの“ルール”だけでは十分な活性向上が得られない場合、どのように設計戦略を再構築すべきでしょうか。
本セミナーでは、自由エネルギー摂動（FEP+）を活用した “predict-first” アプローチにより、SOS1 阻害剤創製において新たな設計指針を見出した事例をご紹介します。FEP+ シミュレーションを通じて、結合ポケット周辺に位置する溶媒露出型の酸性残基 (E906、E909）とのソルトブリッジ形成が、最大 750 倍の活性向上につながる重要な相互作用であることを明らかにしました。
本事例は、従来は設計対象とされにくかった領域を戦略的に活用することでポテンシーを大幅に向上できる可能性を示すものであり、他の創薬ターゲットへの応用も期待されます。SOS1 プログラムの具体的な検討プロセスとともに、溶媒露出型ソルトブリッジ相互作用を活用した新たな創薬戦略について解説します。
Senior Principal Scientist, Computational Chemistry, Therapeutics Group, Schrödinger
プリンストン大学で化学学士号を取得し、ニューヨーク大学メディカルスクールで神経科学の博士号を取得。 現在は、主任科学者として、低分子創薬プロジェクトをリードしている。
Many R&D teams are hindered from adopting AI/ML due to the complexity of software tools, steep learning curves, and limited data science support. Schrödinger’s Materials Science Suite is designed to address these challenges by providing a unified and easy-to-use AI/ML platform, powered by state-of-the-art ML technology and backed by a dedicated scientific support team.
Join our upcoming webinar to learn how your R&D organization can remove adoption barriers, accelerate discovery cycles, and align with national AI initiatives. In this webinar, we will demonstrate how MS Informatics, Formulation ML, and Formulation Optimization make advanced property prediction, model building, and ML-driven design of experiments simple, fast, and accessible – even for non-experts. We will showcase how easy it is to apply these tools using experimental datasets across broad MS applications, including formulations, consumer goods, batteries, pharmaceuticals, and beyond.
Join us and see live demos on:
Who should attend:
Senior Principal Scientist, Materials Science Product and Discovery, Schrödinger
Anand Chandrasekaran joined Schrödinger in 2019 and he is currently the Product Manager of MS-Informatics. His expertise is in applying machine learning to different areas in Materials Science and computational modeling. He graduated from the group of Prof.Nicola Marzari in the Swiss Federal Institute of Technology, Lausanne with a PhD in Materials Science. Before joining Schrödinger, Anand also worked in the group of Prof. Rampi Ramprasad on a number of topics including polymer informatics, machine-learning force-fields, and machine-learning for electronic structure calculations.
AIを活用したマテリアルズ・ディスカバリー（材料探索）は、もはや実験的な取り組みではなく、国家レベルの新たなスタンダードとして定着しつつあります。
先般の米国における「Genesis Mission」の始動により、AI、ハイパフォーマンス・コンピューティング（HPC）、そして統合された科学データインフラを通じた材料探索の加速が、国家的なコミットメントとして打ち出されました。材料イノベーションの最前線に立つR&Dチームにとって、今はまさに計算科学のアプローチ（Computational Workflows）をご自身のパイプラインに統合する絶好の好機と言えます。
しかしながら、多くのR&D現場では、ソフトウェアの複雑さ、習得にかかる学習コストの高さ、そしてデータサイエンス面でのサポート不足といった課題により、AI/MLの本格的な導入が阻まれているのが現状です。
シュレーディンガーの Materials Science Suite は、最先端の機械学習（ML）技術を搭載した、統合型かつユーザーフレンドリーなAI/MLプラットフォームを提供することで、これらの課題を解決します。さらに、専門のサイエンティフィック・サポートチームが皆様を強力にバックアップいたします。
本ウェビナーでは、組織におけるAI導入の障壁を取り除き、研究開発サイクルを加速させ、世界的なAI活用の潮流といかに足並みを揃えていくかについて解説します。
具体的には、MS Informatics、Formulation ML、そして Formulation Optimization を活用し、高度な物性予測、モデル構築、そしてML駆動型の実験計画法（DoE）を、データサイエンスの専門家でなくとも、いかに「シンプルかつ高速」に実行できるかをデモンストレーションします。 また、配合（処方）、消費財、バッテリー、医薬品など、幅広い材料科学分野の実験データセットに対し、これらのツールを容易に適用できることを実例を交えてご紹介します。
【主なデモンストレーション内容】
※本動画の視聴にあたり、以下の点にご留意ください。
Senior Scientist, Schrödinger
