The unified antibody workbench: Optimizing antibody candidate selection through centralized team collaboration

Posted on March 17, 2026April 21, 2026 by Anonymous

Webinar

The unified antibody workbench: Optimizing antibody candidate selection through centralized team collaboration

Date & Time

April 21st, 2026
15:00 CEST | 14:00 BST

Location

Virtual

Antibody discovery is a high-stakes race often slowed down by fragmented data. When experimental results, 3D structures, and sequence alignments live in separate silos, teams lose critical time manually merging spreadsheets and chasing missing metadata. This “information gap” leads to delayed decision-making and missed opportunities in candidate selection.

LiveDesign for Biologics is a web-based, centralized project management hub designed to unite the entire discovery team. By democratizing expert modeling tools, bench scientists can visualize 3D complexes and predict developability parameters alongside their actual lab results with ease.

Join us for an interactive webinar where we showcase the power of LiveDesign for Biologics in a live demo and walk you through how you can quickly start benefiting from this powerful enterprise informatics platform today!

Highlights:

Centralized Triage: Learn how to unite all experimental assay data (Biacore, Luminex) with in silico predictions in one live report
3D Visualization for the Bench: Discover how to assess reactive residues and structural liabilities directly in-platform without being a computational expert
Real-Time Collaboration: Explore how auto-updating live reports and shared dashboards keep entire teams aligned on the most promising leads no matter where they are
See the Platform in Action: Live demo to walk you through how you can conduct your antibody discovery workflow in LiveDesign for Biologics

Who should attend:

Bench scientists, antibody engineers, and R&D project leads who are looking to move away from disconnected data tools and toward a collaborative, model-aware discovery ecosystem

Our Speakers

Caroline Habib

Senior Strategic Deployment Manager, Schrödinger

Caroline Habib joined Schrödinger in 2024 and partners with Pharma and Biotech organizations to maximize the impact of LiveDesign for Biologics, ensuring technical requirements are seamlessly aligned with real-world discovery and development workflows. Prior to joining Schrödinger, Caroline spent years as a bench scientist in drug discovery before transitioning into a Program Manager role. In that capacity, she integrated computational techniques with traditional laboratory methods to lead and accelerate partner-led discovery programs. She completed her BS in Human Biology and her MS in Drug Development at UC San Diego.

Cindy Gerson

Senior Director, Enterprise Product Management, Schrödinger

Cindy Gerson, senior lead product manager, enterprise informatics, joined Schrödinger in 2022. In her role, she leads the LiveDesign for Biologics development efforts – using her extensive knowledge and years of first-hand experience at the lab bench to design software tools that expedite and improve biologics discovery workflows. Prior to joining Schrödinger, Cindy worked at Regeneron Pharmaceuticals in the field of monoclonal antibody-based therapeutics discovery, where she developed, optimized, and executed platforms for the isolation of target-specific antibodies. She completed her BS in Biomedical Engineering at Columbia University and her MS in Bioengineering at Georgia Tech.

Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields

Posted on March 17, 2026April 15, 2026 by Anonymous

Webinar

Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields

Date & Time

May 12th, 2026
15:00 CEST | 14:00 BST | 9:00 EDT

Location

Virtual

The problem:

Developing next-generation energy storage solutions requires a deep understanding of complex, multiscale phenomena—from ion transport in electrolytes to the reactive formation of the solid-electrolyte interphase (SEI). Historically, researchers have been forced to choose between two extremes: the high accuracy but prohibitive computational cost of Density Functional Theory (DFT), or the speed of classical force fields that often lack the “physics” necessary to capture reactive events or complex chemistries. This “simulation gap” delays time-to-market and limits the ability to explore a vast chemical space.

The solution:

This webinar introduces Schrödinger’s state-of-the-art machine learning force field (MLFF) framework, featuring the MPNICE (Message Passing Network with Iterative Charge Equilibration) and QRNN (Charge Recursive Neural Network) architectures. By combining the accuracy of physics-based modeling with the transformative speed of machine learning, Schrödinger provides a “best-of-both-worlds” solution that eliminates traditional trade-offs. We will present live demos showcasing applications of MLFFs for accurate modeling of complex systems including liquid and solid-state electrolytes.

Key highlights:

Rapid Efficiency: Utilize GPU-accelerated engines like Desmond to accelerate the MD simulations, enabling accurate modeling of complex systems like electrolyte formulations and cathode coatings
Near-DFT Accuracy at Scale: Achieve quantum-level precision for energy and force predictions while simulating large systems at timescales previously reserved for classical MD
Unrivaled Tunability: Unlike “black-box” models, Schrödinger’s MLFFs are highly customizable, allowing researchers to incorporate explicit electrostatics and iterative charge equilibration to model ionic liquids and battery interfaces with high fidelity
Seamless Usability: Integrated within the intuitive Schrödinger Materials Science platform, these tools allow users to deploy advanced digital workflows without machine learning expertise

Our Speaker

Garvit Agarwal

Scientific Lead, Energy Storage Materials Science Group, Schrödinger

Garvit Agarwal is Senior Scientist and Scientific Lead for Energy Storage at Schrödinger, working to extend and apply molecular modeling tools for the accelerated discovery of next-generation clean energy technologies. Garvit obtained his Ph.D. in Materials Science and Engineering from the University of Connecticut. He worked as a post-doctoral researcher in the Materials Science Division at Argonne National Laboratory prior to joining the Materials Science team at Schrödinger.

Standing out in a competitive landscape: The power of structure-based biologics design

Posted on March 17, 2026April 23, 2026 by Anonymous

Webinar

Standing out in a competitive landscape: The power of structure-based biologics design

Date & Time

May 7th, 2026
15:00 CEST | 14:00 BST | 9:00 EDT

Location

Virtual

In a competitive landscape, the success of a biologics program depends on the ability to identify high-quality candidates early. Traditional experimental screening approaches are often limited in that they are directed to a singular function of the protein, carry liabilities, or have relatively low hit rates. Schrödinger’s protein design toolkit, powered by Protein FEP+, offers a way to rapidly ideate and assess multiple key features of best-in-class therapeutics. Through computational optimization with advanced, but easy to use, in silico methods, biologists can de-risk molecules and improve experimental hit rates. Affinity, pH dependent affinity, specificity, stability, developability, and more can be evaluated in silico, enabling more efficient prioritization of molecules and delivering superior biologics with a >4x speed-up in development cycles.

Join our upcoming webinar to learn how to leverage advanced in silico methods to de-risk your molecules and increase your experimental success rates.

Highlights:

Intro to advanced in silico methods: An overview of high-fidelity structure-based modeling for modern biologics discovery
How to de-risk and optimize hit rates: How high-accuracy computational prioritization ensures you spend lab time only on the most promising candidates
FEP+ 101 for biologists: Understanding the physics-based workflow for high-accuracy affinity and stability predictions across diverse modalities
See it in action: A walkthrough of in silico lead optimization and residue scanning workflows in a real-world design context

Who should attend:

Bench Biologists and Protein Engineers looking for easy-to-use, comprehensive models to surpass the limitations of empirical screening
Current MM-GBSA Users seeking higher-fidelity predictions to identify superior variants and gain a competitive advantage
R&D Directors and VPs focused on engineering best-in-class biologics and accelerating their path to the clinic
Principal Investigators interested in modernizing their research with high-precision, structure-based design workflows

Our Speaker

Dan Cannon

Director, Product Management, Applications Science, Schrödinger

Dan Cannon, Director, Head of Biologics Modelling is responsible for advancing Schrödinger’s biologics modelling platform capabilities. He is also the Product Manager for Schrödinger’s Protein Design Services. Dan received his Ph.D. from the University of Strathclyde in Glasgow, UK, under the supervision of Prof. Tell Tuttle. In 2016, he began working at MedImmune (now AstraZeneca) in Cambridge, UK, using computational approaches for therapeutic protein design before joining Schrödinger in August of 2018 as an Applications Scientist. Dan continues to publish novel approaches towards structure-based protein design and develop innovative computational solutions for the rational design of biologics.

CPHI Japan 出展 @東京ビッグサイト

Posted on March 17, 2026March 17, 2026 by Anonymous

Event

【4月21日(火)～23日(木)】CPHI Japan 出展

Date & Time

April 21st-23rd, 2026

Location

Tokyo, Japan

創薬課題を短期間・高精度で解決する受託解析

― 計算化学の力で創薬のスピードと成功率を飛躍的に向上 ―

シュレーディンガーの創薬受託解析サービスは、実験並みの精度を誇るFEP+やGlideなど最先端の計算化学技術を駆使し、短期間・定額で成果を提供します。仮想スクリーニング、リード最適化、ADMET予測、結晶多形予測など、創薬初期から開発後期の各段階を効率化。30年以上の研究開発実績と豊富なノウハウを活かし、貴社の創薬課題解決と研究スピードアップを強力にサポートします。創薬の未来を共に切り拓きませんか？

シュレーディンガー受託解析の特徴

短期間・定額で高精度な成果　実験並みの精度を誇る FEP+ や Glide を活用し、効率的かつコストを抑えた解析を提供。
創薬研究を加速する多彩な解析メニュー　仮想スクリーニング、リード最適化、ADMET 予測、結晶多形予測など、創薬プロセス全体を支援。
30年以上の実績と知識移転　蓄積された計算化学ノウハウを活かし、結果だけでなく活用の経験値も提供。

展示ブースでは、先進の技術について専門技術者が解説し、お客様からのご質問にお答えします。

【展示会情報】
展示会名：CPHI Japan(国際医薬品開発展)
会期：2026年4月21日～23日
会場：東京ビッグサイト　東1ホール
小間番号： 1E-03
出展カテゴリー：アウトソーシング

BioTechX 2026

Posted on March 16, 2026March 16, 2026 by Anonymous

Conference

BioTechX 2026

Date & Time

October 6th-8th, 2026

Location

Basel, Switzerland

Schrödinger is excited to be participating in the BioTechX conference taking place on October 6th – 8th in Basel, Switzerland. Join us for a presentation by Steven Jerome, Executive Director, Life Science Software at Schrödinger, titled “Predictive Toxicology: Rational Digital Toxicology in the Cloud with a New AI-Accelerated Physics-Based Workflow.”

Presentation

Predictive Toxicology: Rational Digital Toxicology in the Cloud with a New AI-Accelerated Physics-Based Workflow

Speaker:
Steven Jerome, Executive Director, Life Science Software, Schrödinger

Abstract:
By one estimate, unmanaged toxicity is responsible for roughly 30%1 of all drug discovery project failures. The adoption of experimental screening panels have contributed to the overall improved safety profile of drugs on the market. However, the high cost and latency associated with performing these screens means that such panels are run later in the pre-clinical discovery process and cannot be effectively incorporated into hit finding and lead-optimization stages of the project. To meet the demand for off-target screening during the design process, many teams deploy digital toxicology screening in the form of ligand-based machine learning models. These models, which are fast and inexpensive to operate are typically limited by poor generalizability to ligand matter dissimilar from data used to train the models and are missing the protein context to help designers dial-out liabilities rationally. We present a novel in-silico, physics-based solution for the identification and mitigation of off-target liabilities that constructs a full 3D, atomistic, representation of the ligand interacting with the target and leverages free energy calculations to model off-target binding. Molecules can be evaluated against a single off-target or a panel of representative targets in a screening mode. Calculations are run in the cloud, eliminating any need for local hardware. AI and ML models trained to the physics-based predictions have significant potential to enable high-throughput application in the near future. Already, this workflow has been successfully applied to a wide range of relevant targets across many protein classes. Here, we present both retrospective validation from literature data and prospective application to internal drug discovery projects, where the workflow has seen significant impact throughout our internal drug discovery pipeline, emphasizing the efficient resolution of tox-related liabilities in CYP3A4 and hERG.

Schrödinger Medicinal Chemistry Symposium 2026

Posted on March 12, 2026April 23, 2026 by Anonymous

June 16th, 2026

Cambridge, Massachusetts

Bristol Myers Squibb

250 Water Street Cambridge, MA 02141

Join us for the inaugural Schrödinger Medicinal Chemistry Symposium, an event specifically curated for the medicinal chemistry community.

This full-day symposium will highlight the powerful synergy between traditional medicinal chemistry and modern digital workflows.

The program will feature technical presentations and drug discovery case studies from industry leaders, along with a panel discussion and a hands-on workshop.

The event will also include a reception and dedicated networking opportunities. More specific information on the agenda will be added as it becomes available.

Agenda:

Accelerated discovery of a carbamate scaffold Cbl-b inhibitor using generative models and structure-based drug design (Tandem Talk)

Taylor Quinn, Associate Principal Scientist, Computational Chemistry, AstraZeneca
Alex Chinn, Senior Scientist, Medicinal Chemistry, AstraZeneca

Tandem Talk

Derun Li, Executive Director, Nimbus Therapeutics
Justin Caravella, Senior Director, Computational Chemistry, Nimbus Therapeutics

Structure-based discovery of imidazo[4,5-c]pyridine SARM1 modulators showing paradoxical activation (Tandem Talk)

Adam Levinson, Director, Medicinal Chemistry, Schrödinger
Steven Albanese, Research Leader, Computational Chemistry, Schrödinger

Beyond the lead: Medicinal chemistry optimization of potent and platelet-sparing Bcl-xL degraders

Baljinder Singh, Senior Scientist, Medicinal Chemistry, AstraZeneca

Multi-parameter optimization of p38/MK2 molecular glues: An interactive design challenge (Workshop)

Bridging the gap: Expanding the reach of computational chemistry in drug discovery (Panel)

Veerabahu Shanmugasundaram, Executive Director, Bristol Myers Squibb
Maria Jesus Blanco Senior, Vice President – Head of Drug Discovery, Atavistik Bio
Moderator: Steven Albanese, Research Leader – Computational Chemistry, Schrödinger

Organizing Committee:

Melissa Buskes, Senior Scientist, Medicinal Chemistry, Atavistik Bio
Kevin Cusack, Retired Research Fellow, AbbVie
Adam Levinson, Director, Medicinal Chemistry, Schrödinger
Agustina Rodriguez-Granillo, Director, Applications Science, Schrödinger
Wade Miller, Senior Manager, Education, Schrödinger

Venue:

Bristol Myers Squibb
250 Water Street Cambridge, MA 02141.

Frontiers in Digital Chemistry: Industry Summit

Posted on March 12, 2026April 8, 2026 by Anonymous

June 9th-10th, 2026

Schrödinger NYC Office

Schrödinger is pleased to host the inaugural Frontiers in Digital Chemistry: Industry Summit, an in-person gathering for industry professionals in the materials-science digital-chemistry community.

This two-day event convenes scientists, technical leaders, and R&D decision-makers from across industries — including polymers, consumer packaged goods (CPG), specialty chemicals, energy, petrochemicals, thin film processing and advanced materials. Together, we will explore how the integration of AI, physics-based modeling, and computational workflows is reshaping materials innovation and accelerating discovery.

Hosted at our New York City headquarters overlooking Times Square, the event brings digital chemistry discussions to the heart of Manhattan.

The event will begin on June 9 at 1:30 PM, with afternoon sessions followed by an evening dinner. Sessions will continue on June 10 with a full day of programming, concluding at 5:15 PM.

Agenda

Tuesday, June 9th

1:30 – 2:00 Welcome & Opening Remarks

2:00 – 5:15 Industrial and Schrödinger Presentations

6:00 – 9:00 Networking & Dinner

Wednesday, June 10th

9:00 – 11:15 Industrial and Schrödinger Presentations

11:15 – 12:00 Panel Discussion

12:00 – 1:15 Lunch

1:30 – 4:00 Industrial and Schrödinger Presentations

4:00 – 5:15 Concluding Reception: Informal Chats with Schrödinger Technical Experts, Product Teams, and Leadership

What to Expect

The summit is designed as an interactive and forward-looking forum that blends technical depth with strategic discussion. The agenda will feature:

Presentations from Schrödinger scientists and leadership
Perspectives from industry practitioners
A moderated panel discussion
Structured and informal peer-to-peer exchange
Select interactive or workshop-style sessions

Beyond formal sessions, the event is intentionally structured to encourage meaningful dialogue. Attendees will engage directly with fellow practitioners and Schrödinger’s scientific and product leadership to exchange insights and help shape future directions in digital chemistry.

Who Should Attend

Both Schrödinger users and non-users are welcome. This event is intended for industry professionals involved in:

Materials research and development
Computational chemistry and molecular modeling
AI and machine learning applied to materials science
Digital transformation of industrial R&D
Innovation across polymers, CPG, chemicals, energy, and related sectors

The summit aims to foster open, cross-industry dialogue — uniting diverse perspectives around a shared goal: advancing materials innovation through digital chemistry.

Venue Location

Schrödinger, NYC office,
1540 Broadway 21st floor,
New York, NY, USA

Amplifying medicinal chemist impact with large-scale ideation, FEP+, machine learning, and retrosynthesis through LiveDesign

Posted on March 6, 2026April 16, 2026 by Anonymous

Webinar

Amplifying medicinal chemist impact with large-scale ideation, FEP+, machine learning, and retrosynthesis through LiveDesign

Date & Time

April 16th, 2026
15:00 CEST | 14:00 BST

Location

Virtual

Medicinal chemistry is an increasingly complex orchestration of high-stakes tasks – interpreting project SAR, ideating compounds that satisfy both affinity and ADMET profiles, and managing the logistics of synthesis and assay reporting. When computational tools are siloed from the medicinal chemist’s primary workspace, the impact of advanced modeling is diluted, and the path to a development candidate (DC) becomes uncertain.

Join us to see how Schrödinger’s Enterprise Informatics Platform, LiveDesign, serves as the single terminal to bridge this gap. By integrating the complete computational pipeline, LiveDesign enables medicinal chemists to adopt a fully “predict-first” paradigm. This approach amplifies the impact of the entire project team, from modelers to leadership, by centralizing decision making and collaboration. By equipping your team with predictive insights throughout the discovery process, you can make MPO optimization more efficient, reduce the total number of synthesized compounds, and significantly shorten DMTA cycles and the journey to a DC.

Key Highlights:

Strategic DMTA Acceleration: How to utilize AutoDesigner and Active Learning FEP+ to vet thousands of ideas before committing to synthesis
Seamless MPO Workflows: Balancing potency and ADMET profiles simultaneously within the LiveDesign interface to avoid late-stage failures
Synthetic Reality Checks: Utilizing RetroSynth to bridge the gap between high-scoring digital designs and practical bench-top feasibility
LiveDesign in Action: A live walkthrough of a “lead-to-DC” scenario, demonstrating how the LiveDesign Assistant and integrated modeling tools allow a chemist to move from a raw SAR table to a prioritized, tractable synthesis list in a single session

Who Should Attend:

Medicinal Chemists looking to leverage the industry’s most accurate potency predictions and ML models within their daily design workflow
Project Team Leads seeking to maximize budget efficiency and eliminate the technical silos that delay candidate selection
Computational Chemists aiming to scale their impact by deploying validated, gold-standard models for organization-wide use

Our Speakers

Jason Castaneda

Executive Director, Account Management, Schrödinger

Márton Vass

Senior Principal Applications Scientist, Schrödinger

Márton Vass is the Global Lead of the All Access Applications Science team at Schrödinger. He works on the development and deployment of scientific workflows in LiveDesign, with special focus on FEP+ and Desmond MD in LiveDesign, and his research is focussed on automating the use of ML protein folding and cofolding methods for drug discovery applications. Before joining Schrödinger Márton received his PhD at the Budapest University of Technology and Economics while working at Gedeon Richer Plc mid-sized pharmaceutical company. He also held a postdoctoral position at the Vrije Universiteit Amsterdam implementing cheminformatics algorithms for GPCR drug discovery in Knime, after which he joined BenevolentAI in London to apply machine learning augmented drug discovery methodologies, and to lead the development of protein structure-based machine learning tools in the company.

ICDT 2026

Posted on March 6, 2026March 6, 2026 by Anonymous

Conference

ICDT 2026

Date & Time

March 31st – April 3rd, 2026

Location

Chongqing, China

Schrödinger is excited to be participating in the International Conference on Display Technology, ICDT 2026 taking place on March 31st – April 3rd in Chongqing, China. Stop by Booth 3A4 and catch Hadi Abroshan, Principal Scientist II, Materials Science Product and Discovery presenting in Session 43: OLED – Simulations 1 on April 2, 15:40–16:00.

Presentation

APR 2 | 15:40

Session 43: OLED – Simulations 1

Accelerating OLED Design: Integrating Machine Learning and Physics-based Simulation

Speaker:
Hadi Abroshan, Principal Scientist II, Materials Science Product and Discovery

Abstract:
The experimental development of innovative OLED device architectures and material compositions is time-consuming, labor-intensive, and resource-heavy due to the complexity and cost associated with fabrication, characterization, and analysis. Predictive modeling offers a powerful alternative, enabling efficient and targeted evaluation of devices across broad design spaces.

Introducing RetroSynth: Breaking the synthesis bottleneck with AI and physics-based modeling

Posted on March 4, 2026April 22, 2026 by Anonymous

Webinar

Introducing RetroSynth: Breaking the synthesis bottleneck with AI and physics-based modeling

Date & Time

May 6th, 2026
8:00 AM PDT | 11:00 AM EDT | 4:00 PM BST | 5:00 PM CEST

Location

Virtual

Join us for the introduction of RetroSynth, Schrödinger’s AI-driven synthesis planning platform. RetroSynth is engineered to accelerate and scale conventional retrosynthesis by harnessing advanced deep learning algorithms. RetroSynth uses a distributed cloud-native Monte Carlo tree search (MCTS) architecture to perform highly exhaustive searches to predict and score optimal, accurate, and cost-efficient synthetic pathways. RetroSynth helps you move from complex chemical targets to actionable synthesis plans accurately and efficiently. By integrating real-time building block data with AI and physics-based modeling, RetroSynth unlocks accurate retrosynthetic analysis across millions of de novo designed molecules, leading to massive project acceleration and cost savings in hit identification and lead optimization.

Key Highlights:

Introduction to RetroSynth: Schrödinger’s product manager will give a detailed description of how RetroSynth works and its advantages over conventional retrosynthesis solutions
Live Demo: See how RetroSynth performs in action
Ask questions: Direct technical discussion with our experts

Who Should Attend:

Medicinal Chemists: Looking to accelerate lead optimization and SAR exploration
Process Chemists: Focused on identifying scalable and cost-effective synthetic routes early in development
Computational Chemists: Interested in the integration of ML/AI frameworks into standard R&D workflows
R&D IT Directors: Evaluating enterprise-grade cheminformatics solutions for chemical synthesis

Our Speakers

Aditya Kaushik

Principal Scientist I, Schrödinger

Aditya Kaushik is an ML Research Scientist and the lead developer for the generative design and retrosynthesis technologies at Schrödinger. His primary focus is on the research, development, and integration of machine learning approaches to accelerate and optimize Design-Make-Test-Analyze (DMTA) cycles in active drug discovery programs. He received his B.S. from Johns Hopkins University, where he double majored in Computer Science and Chemical & Biomolecular Engineering.

Sathesh Bhat

Executive Director, Therapeutics Group, Schrödinger

Sathesh Bhat, Ph.D., executive director in the therapeutics group, joined Schrödinger in 2011. He is responsible for overseeing computational chemistry efforts on internal and partnered drug discovery programs at Schrödinger. Previously, Sathesh worked at both Merck and Eli Lilly leading computational efforts in several drug discovery programs. He obtained his Ph.D. from McGill University, which involved developing structure-based methods to predict binding free energies. Sathesh has co-authored multiple patents and publications and continues to publish on a wide variety of topics in computational chemistry.

Beyond the bench: Getting started with molecular dynamics simulations

Posted on March 4, 2026April 9, 2026 by Anonymous

Webinar

Beyond the bench: Getting started with molecular dynamics simulations

Date & Time

April 9th, 2026
15:00 CEST | 14:00 BST

Location

Virtual

Materials science R&D is entering a new era where experiment and computation work together to accelerate discovery. Molecular Dynamics (MD), especially when used in tandem with Machine Learning (ML), is transforming how scientists understand structure–property relationships, predict performance, and reduce costly trial-and-error. Experimentalists can now simulate interfaces, defects, polymers, and electrolytes before stepping into the lab.

Modern MD engines, like Desmond from Schrödinger, makes scalable, high-performance simulations accessible, linking atomic insight to real-world materials challenges. By integrating ML with physics-based MD, researchers can screen candidates faster and make smarter decisions.

Join Schrödinger’s Katie Dahlquist, as she’ll show you how Desmond can be used to improve your development. Learn from live demos for materials formulations and predictive workflow. During the webinar, you will be able to ask questions and leave with an understanding of how to start incorporating MD into your R&D strategy.

Webinar Highlights:

Discover an accessible, intuitive platform for starting MD simulations
Learn how Desmond adds value to experimental studies
See digital compatibility screening in action
Get started with molecular dynamics simulations using Materials Science Maestro

Our Speaker

Katie Dahlquist

Principal Scientist I, Schrödinger

Katie Dahlquist is a Principal Scientist leading the materials science education efforts at Schrödinger. Since joining the company, she has played a role in developing and delivering our molecular modeling for materials science online certification courses, contributing to our collection of materials science tutorials, and leading workshops and customer training sessions. Katie’s expertise lies in density functional theory calculations and molecular dynamics simulations.

Embracing a new era of toxicity screening: Atomic-resolution modeling to mitigate off-target liabilities

Posted on March 4, 2026April 6, 2026 by Anonymous

Webinar

Embracing a new era of toxicity screening: Atomic-resolution modeling to mitigate off-target liabilities

Date & Time

March 31st, 2026
14:00 BST | 15:00 CEST

Location

Virtual

Late-stage discovery failures due to off-target liabilities, particularly hERG, CYP, and nuclear receptor interactions, remain a primary driver of project delays and sunk costs. Traditional predictive methods often act as “black boxes,” providing binary pass/fail flags without the mechanistic context needed to guide chemical synthesis. Without an atomically accurate understanding of why a molecule is hitting an anti-target, medicinal chemistry teams are often forced into blind “guess-and-check” cycles, risking both potency and safety.

Join us for a technical overview of Schrödinger’s Predictive Toxicology solution. This session will demonstrate how physics-based, atomic-resolution modeling transforms toxicology from a reactive “filter” into a proactive “design tool.” We will explore how to move beyond simple predictions to generate actionable binding hypotheses, allowing teams to surgically engineer out liabilities while maintaining primary activity. By integrating these insights directly into the DMTA cycle, discovery teams can significantly reduce synthesis costs and accelerate the path to a clean lead.

Key Highlights:

Mechanistic Attribution: Learn how to move from binary toxicity “flags” to atom-level structural rationales
Accelerated DMTA Cycles: See how predictive structural models can reduce experimental timelines by >10X
Structural SAR: Strategies for performing surgical chemical modifications to mitigate hERG and CYP risk without sacrificing potency
Live Demo: See the platform in action, showcasing how quickly you can go from a toxic prediction to a viable design modification

Who Should Attend:

Medicinal Chemists looking to rationalize and design around off-target SAR
Toxicologists interested in mechanistic, structural-based risk assessment
Computational Chemists & Modelers seeking to integrate high-fidelity tox predictions into their design workflows
Discovery Leads focused on reducing late-stage attrition and optimizing project budgets

Our Speakers

Ed Miller

Vice President, Life Science Software, Schrödinger

Edward Miller, Senior Director of Protein Structure Modeling, joined Schrödinger in 2014, and is responsible for advancing the domain of applicability of structure-based drug discovery into challenging targets and off-targets. Dr. Miller obtained his PhD from Columbia University, where he was awarded a DOE research fellowship. His thesis work with Professor Richard Friesner involved developing methods to accurately model loop conformations across a broad array of protein families. His recent work has been focused on methods development for induced fit docking and protein structure refinement.

Steven Albanese

Research Leader, Computational Chemistry, Therapeutics Group, Schrödinger

Steven Albanese joined Schrödinger in 2019 as a Computational Chemist in the Therapeutics group, with a focus on the application of Schrödinger’s computational platform to small molecule drug discovery projects. Dr. Albanese received his PhD from Gerstner Sloan Kettering at Memorial Sloan Kettering Cancer Center, where he studied with Dr. John Chodera. His thesis work focused on the application of free energy calculations to predict resistance and selectivity for small molecule kinase inhibitors. He has continued his research on predicting drug resistance, and is an inventor on a number of small molecule patents as well.