Postdoctoral Fellow (Cheminformatics/AI-ML at NCATS)

NCATS, a major translational research component of NIH, seeks applications from outstanding candidates to fill a computational chemistry/cheminformatics postdoctoral fellow position in the Therapeutic Development Branch (TDB).

The selected fellow will work under the co-mentorship of Wei Zheng, Ph.D. (Biology Group Leader) and Min Shen, Ph.D. (Informatics Group Leader), in a team environment focused on drug development. The fellow will focus on applying state-of-the-art computational chemistry techniques—including molecular modeling, molecular dynamics, artificial intelligence/machine learning (AI/ML) and virtual screening—to help design, identify, and develop new therapeutic agents. The successful candidate will have the opportunity to contribute to high-impact projects and work closely with multidisciplinary teams.

Core Responsibilities

• Develop and implement AI/ML models to predict compound activity, selectivity, and other properties relevant to drug discovery.
• Conduct structure-based and ligand-based drug design studies to identify novel bioactive compounds.
• Apply molecular docking, molecular dynamics and free-energy calculations to assess compound binding and stability.
• Perform virtual screening of large and diverse chemical libraries to identify novel chemotypes for prospective experimental testing.
• Analyze and integrate HTS, dose-response, counterscreen, and follow-up assay data to identify structure-activity relationships (SAR) and guide hit prioritization.
• Work closely with experimental scientists to guide compound synthesis and biological testing based on computational findings.
• Collaborate with researchers internally and externally in other NIH institutes and universities.
• Present research findings in internal meetings and at external scientific conferences and contribute to peer-reviewed publications.

Prospective applicants should possess a Ph.D. in computational chemistry, cheminformatics, computer science, data science, bioengineering, pharmacology or related discipline, with demonstrated experience in data-driven research, including machine learning or statistical modeling.

Strong programming skills in languages such as Python or R are required, along with excellent written and oral communication skills and the ability to work both independently and collaboratively in a multidisciplinary research environment.

Experience working with large-scale biological or chemical datasets, QSAR modeling, or familiarity with AI/ML methods and/or in vitro experimental platforms is preferred.

This position is not eligible for full-time remote work, and NIH does not permit trainees to telework from overseas locations.