NLM Colloquia on Biomedical Data Science and
Computational Biology Research
NLM welcomes Pratyush Tiwary, PhD, Millard and Lee Alexander Professor at the University of Maryland, College Park, to present his lecture entitled “Integrating Generative AI with Statistical Mechanics for Predicting Biomolecular Structure and Properties Across Temperature, Pressure, Chemical Potentials” at the NLM Colloquia on Biomedical Data Science and Computational Biology Research. Please join us on January 29, 2025, at 11:00am ET in the NIH Natcher Building (45), Balcony B, and online via NIH Videocast.
The NLM Colloquia is a series of scientific lectures featuring experts from across the bioinformatics community who present their research in the rapidly evolving fields of biomedical data science and computational biology research and discuss how it contributes to advancing biomedical discovery. This series is presented by NLM’s Division of Intramural Research, a premier hub of innovation for computational biology and biomedical data science.
Integrating Generative AI with Statistical Mechanics for Predicting Biomolecular Structure and Properties Across Temperature, Pressure, Chemical Potentials
Event Date: Wednesday, January 29, 2025
Time: 11:00am–12:00pm
Speaker: Pratyush Tiwary, PhD
Location: NIH Natcher Building (45), Balcony B, and virtual via NIH Videocast
Abstract:
Structure prediction tools using generative artificial intelligence (AI) have significantly advanced, offering rapid predictions of the most stable structure for generic proteins/RNA and even generating ensembles with dynamics. This might suggest that molecular dynamics (MD) and statistical mechanics are now maybe obsolete. However, I will demonstrate why these methods remain critical for ensuring AI approaches are both predictive and reliable for biophysics. I'll discuss how current AI predictions can sometimes result from memorization or hallucination and show how integrating generative AI with enhanced MD and statistical mechanics provides a more predictive, though slower, alternative to using AI alone. Examples will include kinases and RNA, revealing thermodynamic and dynamic properties such as drug residence times, conformational populations, mutation effects, and melting curves derived from chemical identity and force fields. Lastly, I’ll illustrate how these integrated methods enable predictions of biomolecular properties under thermodynamic conditions far from training data, including temperature, pressure, and chemical potential.
Speaker Bio:
Dr. Tiwary is the Millard and Lee Alexander Professor at the University of Maryland, College Park, where he also leads the Center for Therapeutic Discovery at the Institute for Health Computing. His lab combines AI and statistical physics to solve problems of human health and energy relevance. Tiwary received his degrees from IIT-Varanasi and Caltech and completed postdoctoral work at ETH Zurich and Columbia University. He started as an Assistant Professor at Maryland in 2017, then was promoted to tenured Associate Professor in 2022 and Full Professor in 2023. He is a member of the Scientific Advisory Board of Schrodinger and Associate Editor for Journal of Chemical Theory and Computation. He has been recognized through different awards, including the student-nominated Dean's Award for Excellence in Teaching and the Sloan Research Fellowship in Chemistry.
How to Join:
NIH Natcher Building (45), Balcony B
This talk will be broadcast live: NIH Videocast
Interpreting services are available upon request. Individuals with disabilities who need reasonable accommodation to participate in this lecture should contact NLMColloquia@nih.gov or the Federal Relay (1-800-877-8339).
Questions during the presentation can be sent to: NLMColloquia@nih.gov.
Sponsored by:
Richard Scheuermann, PhD
Scientific Director, Division of Intramural Research, National Library of Medicine