Cheminformatics Resources of U.S. Governmental Organizations 2027 Workshop April 12 - 14, 2027

Conference | In Person

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• Date: April 12 - 14, 2027
• Day1: Mon, Apr 12 9:00 a.m. - 04:30 p.m. ET
• Day2: Tue, Apr 13 9:00 a.m. - 04:30 p.m. ET
• Day3: Wed, Apr 14 9:00 a.m. - 04:30 p.m. ET
• Location: Attend In Person or Online; Virtual: Via webcast; In Person: FDA White Oak Campus; 10903 New Hampshire Avenue; Building 31, Room 1503; Silver Spring, MD 20993; United States

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Organizers:
FDA Modeling and Simulation Working Group, Chemical Informatics and Modeling Interest Group

Co-organizers:

• National Institute of Standards and Technology (NIST), Mass Spectrometry Data Center (MSDC)
• Food and Drug Administration (FDA), Human Foods Program (HFP)
• Food and Drug Administration (FDA), Office of the Commissioner (OC), National Center for Toxicological Research (NCTR), Division of Bioinformatics and Biostatistics
• National Institutes of Health (NIH), National Center for Biotechnology Information (NCBI)
• National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS)
• Food and Drug Administration (FDA), Office of the Commissioner (OC), Office of Mission Information Technology Services (OMITS)

Location:
FDA White Oak Campus (Great Room - 1503A) and Virtual

Registration:
Registration is required and limited to Government employees/contractors. Please register to attend the workshop here.

About the Workshop:
FDA’s Chemical Informatics and Modeling Interest Group is hosting a workshop for Government-funded organizations on April 12-14, from 9:00 a.m.–4:30 p.m. ET.

The purpose of the Cheminformatics Resources of U.S. Governmental Organizations 2027 Workshop is to enhance communication and collaboration between the U.S. Government-funded organizations that create and maintain databases, data standards, datasets, scientific approaches and computational resources dealing with chemical structures and properties of molecules and materials.

Session: Application of Cheminformatics to Support Analytical Chemistry
Session chairs: Dr. Tytus Mak (NIST/MSDC), Dr. Karen E. Butler (FDA/HFP)

This session will focus on integrating cheminformatics into analytical chemistry workflows and creating a dialogue for governmental regulatory stakeholders who are implementing qualification and acceptance guidelines. Topics of discussion will include the use of cheminformatics to develop machine learning models for chemical analysis (e.g., nuclear magnetic resonance data, retention time prediction, method amenability, mass spectrometry fragmentation), the design and optimization of chromatographic separations, and the integration of cheminformatics/chemometric tools (including the use of large chemical databases) as part of the regulatory development process for governmental stakeholders. Attendees will gain a deeper understanding of the potential benefits and challenges of using cheminformatics to support analytical chemistry and will leave with practical insights for incorporating these tools into their own research and analysis workflows.

Session: Cheminformatics for New Approach Methodologies (NAMs)
Session chairs: Dr. Huixiao Hong (FDA/NCTR), Dr. Evan Bolton (NIH/NCBI)

The shift from animal testing to New Approach Methodologies (NAMs) has made cheminformatics a key driver of modern chemical safety assessment. By transforming chemical structures into predictive information, computational tools support interpretation of in vitro data, toxicity prediction, and regulatory decision-making. This session highlights advances in computational chemistry, data science, and predictive toxicology, including: (1) AI, machine learning, and QSAR models for toxicity prediction; (2) chemical similarity, applicability domain, and read-across; (3) integration of in vitro, omics, and HTS data; (4) cheminformatics for PBK modeling and IVIVE; and (5) successful regulatory applications of cheminformatics-enabled NAMs.

Session: AI/ML for Drug Repurposing and Drug Discovery
Session chairs: Dr. Ewy Mathé (NIH/NCATS), Dr. Samir Lababidi (FDA/OC/OMITS), Dr. Alexey Zakharov (NIH/NCATS)

This session will explore how artificial intelligence and machine learning are transforming the drug discovery and repurposing landscape by accelerating candidate identification, optimizing hit-to-lead pipelines, and uncovering novel therapeutic indications for existing compounds. Topics of discussion will include deep learning architectures for molecular property prediction, generative models for de novo molecular design, network-based approaches for target identification, and automated literature mining for repurposing hypotheses. Speakers will also address practical challenges in model generalizability, biological data quality, and the integration of predictive models into translational pipelines and regulatory frameworks.

Event Materials:

To be posted