In silico Drug Discovery Workshop

Wed, Oct 23, 2024, 11:00 AM EDT – Thu, Oct 24, 2024, 5:15 PM EDT

About the Workshop

The National Center for Advancing Translational Sciences (NCATS) Assay Guidance Manual (AGM) program is hosting a two-day workshop that will cover a broad range of critical concepts, including practical approaches and best practices, to successfully develop and integrate accurate, robust, and rigorous AI/ML methods/models into drug discovery pipelines and to facilitate discussions around utilization of such methodologies. Many of the speakers are pioneers of covered scientific topics and the listed workshop sessions aim to share the fundamentals and modern knowledge to facilitate learning for both novice and established practitioners. This workshop is jointly organized by NCATS, Biomedical Advanced Research and Development Authority (BARDA), University of California San Diego (UCSD), and The University of North Carolina at Chapel Hill (UNC).

Goals and Objectives

This workshop aims to provide scientists with best practices and standards for rigor in the field of computational drug discovery to enable accurate and reproducible results. This workshop will also cover case studies for AI-driven drug discovery campaigns as well as an overview of new trends and gaps in the field.

Specific learning goals and objectives of this workshop include:

  • Provide participants with data sources and best practices in building and maintaining databases used for developing robust and rigorous AI based drug discovery models/methods.
  • Introduce participants to the available computational methodologies utilized in drug discovery and discuss their utility and limitations.
  • Provide case studies for digital drug discovery and an overview of new trends in the field.
  • Provide guidelines and considerations for developing robust and reproducible in silico models.
  • Discuss challenges in data quality and data sharing as well as affordability, accessibility, transferability, accuracy, and reproducibility of AI-driven computational techniques.
  • Identify gaps in translation of these in silico models to therapies and seed discussions around best practices to help bridge the gaps in the field.


Download the agenda

About the Assay Guidance Manual Program

The AGM program is a world-class source of guidelines and best practices for advancing translational science and research in the preclinical development of novel therapeutics and offers training and insight for those desiring to become translational scientists. This program provides scientists with community-developed best laboratory practices in early translational research, including robust assay development, analytical technologies, data analysis tools and preclinical drug discovery standards. The program also aims to help the preclinical drug discovery workforce understand the value of robust, reproducible and replicable results.

The AGM program provides multiple resources, including the AGM eBook. This manual contains best practices in preclinical drug discovery and is free and publicly available from the National Library of Medicine; its editorial board has members from both the private and public sectors.

The program offers in-person and virtual multiday training workshops and conferences. It also hosts an AGM Preclinical Translational Science Webinar Series. This series highlights distinguished translational scientists who share case studies and information about preclinical translational science.

Workshop Organizing Committee

Rommie Amaro, University of California San Diego (UCSD)

Kyle Brimacombe, NCATS

Abigail Grossman, NCATS

Matthew D. Hall, NCATS

Sarine Markossian, NCATS

Morgan Sherer, Biomedical Advanced Research and Development Authority (BARDA)

Alexander Tropsha, University of North Carolina at Chapel Hill

Alexey Zakharov, NCATS


Alexander Tropsha

KH Lee Distinguished Professor, UNC Eshelman School of Pharmacy, UNC Chapel Hill

Alexandre Varnek

Professor, University of Strasbourg (France)

Alexey Zakharov

Informatics Group Leader, NCATS

Barbara Zdrazil

ChEMBL Coordinator, Chemical Biology Services, European Bioinformatics Institute (EMBL-EBI)

Bryn Taylor

PhD. Scientist, In Silico Discovery, Johnson & Johnson Innovative Medicine

Cheryl Arrowsmith

CSO, Structural Genomics Consortium, University of Toronto

Christopher P. Austin

Senior Vice President of Research Technologies, GSK

Connor W. Coley

Assistant Professor, Massachusetts Institute of Technology

David Baker

Institute for Protein Design, Department of Biochemistry, University of Washington

Gisbert Schneider

ETH Zürich

John D. Chodera

Member, Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center

Marissa Saunders

Senior Director, Data Science, Recursion Pharmaceuticals

Martha S. Head (Marti)

Executive Director, Computational and Data Sciences, Amgen Inc.

Michael Ronzetti


Milad Abolhasani

ALCOA Professor and University Faculty Scholar

Morgan Sherer

Technical Program Manager, Contractor supporting BARDA

Pat Walters

Chief Data Officer, Relay Therapeutics

Rommie Amaro

Rommie E. Amaro, Professor of Molecular Biology, Distinguished Professorship in Computational and Theoretical Chemistry, University of California, San Diego

Russ B. Altman

The Kenneth Fong Professor of Bioengineering, Genetics, Medicine, & Biomedical Data Science, and, by courtesy, Computer Science Senior Fellow, Stanford Institute for Human-Centered AI, Stanford University

Sarine Markossian

Editor-in-Chief, Assay Guidance Manual; Lead, Translational Science Resources Program, NCATS

Sean Ekins

CEO, Collaborations Pharmaceuticals, Inc.

Stephen K. Burley

University Professor and Henry Rutgers Chair

Vicki Feher

Director of CADD, California Team, Novartis BioMedical Research

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