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  4. Cheminformatics Resources of U.S. Governmental Organizations - 10/16/2024
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Workshop | Mixed

Event Title
Cheminformatics Resources of U.S. Governmental Organizations
October 16 - 18, 2024

October 16 - 18, 2024
- ET
- ET
- ET
Event Location
White Oak Campus, Great Room A
FDA Headquarters

10903 New Hampshire Avenue
Silver Spring, MD 20993
United States


  • FDA Modeling and Simulation Working Group
    • Chemical Informatics and Modeling Interest Group 
  • Women of CDER, Artificial Intelligence Interest Group
  • EPA, Center for Computational Toxicology and Exposure
  • NIH, Frederick National Laboratory for Cancer Research (FNLCR), Cancer Data Science Initiatives
  • NIH, National Center for Biotechnology Information, PubChem
  • NIST, Mass Spectrometry Data Center

Location: FDA White Oak Campus, Great Room and Virtual (via Zoom - hyperlink will be sent after registration)

Registration: Registration is required and limited to Government employees/contractors. Please register for the workshop here External Link Disclaimer.

FDA’s Chemical Informatics and Modeling Interest Group, together with Women of CDER, Artificial Intelligence Interest Group, are hosting a workshop for Government-funded organizations on October 16-18, 2024, from 9:00 a.m.–4:30 p.m. ET.

The workshop Cheminformatics Resources of U.S. Governmental Organizations will bring together cheminformaticians working in different branches of the U.S. Government to share experience and challenges in creating and maintaining computational resources pertaining to structures and properties of molecules and materials.

About the Workshop

Session: Application of Cheminformatics to Support Analytical Chemistry   

Session chairs: Dr. Tytus Mak (NIST/Mass Spectrometry Data Center), Dr. Karen E. Butler (FDA/CFSAN),  

 In recent years, cheminformatics has become an integral part of analytical chemistry, playing critical roles in data processing, results interpretation, and visualization of increasingly complex measurements. This session will focus on integrating cheminformatics into analytical chemistry workflows and creating a dialog for governmental regulatory stakeholders that 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., retention time prediction, method amenability, mass spectrometry fragmentation, non-targeted analysis), the interpretation of spectral data, the design and optimization of chromatographic separations, and the integration of cheminformatics tools as a part of the regulatory development process for government.  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: Data science interfaces to Chemistry-oriented Resources  

Session chairs: Dr. Evan Bolton (NIH/NLM/NCBI), Dr. Jie Liu (FDA/NCTR)  

Interoperability and accessibility to cheminformatics resources are important for collaborations and sharing of information, in addition to being the “A” and “I” in FAIR (Findable, Accessible, Interoperable, and Reusable).  Data scientists often need large quantities of information, and FAIR data is not always Globally Open and FAIR (GO-FAIR) data. To access this content, one approach uses web-based tools to enable users to query, extract, and share data within and across organizations. In addition, cloud-based tools (where you create your own private virtual environment) are becoming increasingly available to access scientific content. Each organization develops its own approaches and methods – with some developing many. Because of the variety of methods being used, there is a need and an opportunity to harmonize and standardize interfaces and best practices. This session will focus on introducing the available data-science interfaces to chemical information, how to use them, and explore how they can be improved for the benefit of all.

Session: Predictive Models and Protected Data   

Session chairs: Dr. Eric A Stahlberg (FNLCR/Cancer Data Science Initiatives), Dr. Samir Lababidi (FDA/OC/Office of Data, Analytics, and Research)   

There is growing interest in working with protected or restricted cheminformatics data when creating, evaluating, and using predictive models, particularly in regulatory environments. The session will bring together the disciplines of data science and cheminformatics to share current capabilities and establish future directions for collaborative efforts in the development of predictive models and protected data involving chemical information. Topics covered include federated learning, privacy preserving approaches, role of sequestered data and integrity and transparency. The session will also involve presentations on workflows and frameworks that can be employed, the role of standard datasets and models, and encoding or securing data.

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