Challenges for Applied Mathematics in Structural Biology and Drug Discovery

Abstract

I will discuss (1) work from my academic lab at UCSF, in the pre-deep learning era, focused on challenges in conformational sampling and analysis, including poly-cyclic macrocycles and correlated motions in macromolecules; and (2) challenges that currently interest me in my role as the director of the new AI in Drug Discovery national platform, especially those related to generative AI for drug-like small molecules.  

Biography

Matthew P. Jacobson, Ph.D., was an undergraduate at Stanford, earned his Ph.D. in Physical Chemistry at MIT, and then completed post-doctoral research at Oxford University and at Columbia University. He became a faculty member in the Department of Pharmaceutical Chemistry at UCSF in 2002, received tenure in 2007, and subsequently served as Director of the Graduate Group in Biophysics and then Chair of his department. Matt’s research interests are principally in the area of computer-aided drug design, and more broadly the modeling and simulation of biological molecules, and he is the author of over 200 publications including a textbook and several patents. At UCSF, Matt received the NSF CAREER award, an Alfred P. Sloan Fellowship, and several School of Pharmacy teaching awards, and served on the editorial boards of several journals. Software written by Matt and his collaborators is widely used in the pharmaceutical industry, and he served on the Scientific Advisory Board of Schrödinger, Inc. for 2 decades. Since 2011, he has co-founded 6 biotechnology companies, including Relay Therapeutics (NASDAQ: RLAY) and Circle Pharma. In 2026, Matt moved to Singapore to direct the nascent AI in Drug Discovery program, administered by A*STAR.

Google Scholar: https://scholar.google.com/citations?user=3gyXU5EAAAAJ&hl=en
Pubmed: https://pubmed.ncbi.nlm.nih.gov/?term=matthew+p+jacobson
ORCid: 0000-0001-6262-655X