Ajay N. Jain, PhD
Professor, Department of Bioengineering and Therapeutic Science, UCSF
My research and training efforts are geared toward applications of computation to drug discovery, with an emphasis on making use of multiple methods and data types, exploiting synergy between protein structural data, chemical/target annotation information, and ligand binding affinity data. My laboratory has a particular focus on cancer-related therapeutic discovery, as are part of many collaborators that make use of our computational methods.
I have been at the forefront of computer-aided drug discovery (CADD) since the early 1990's, beginning in biotechnology start-ups such as Arris Pharmaceutical and continuing for over 20 years at UCSF. The central areas of research in the lab are: (1) methods for docking small molecules to proteins using empirically derived scoring functions; (2) methods for inducing the shape of a protein binding pocket given the structures and affinities of ligands that bind the pocket competitively; (3) generalized surface-based approaches to computing molecular similarity, both among small molecules and among proteins; and (4) approaches for very fast conformational search, including macrocycles, optionally including context from NMR restraints or fitting X-ray density maps. All of the approaches share their roots in the use of sophisticated computational algorithms involving object representation, function optimization, and search. My formal training at the PhD-level in Computer Science has been a distinct advantage in this work.
The following highlight recent work that is particularly relevant to the themes within the lab: synergy of diverse methods and data types for drug discovery. (1) We made use of a fast generalization of our ligand-based similarity approach (looking at small molecule surfaces from the outside) to instead quantify the similarity of protein binding sites (by looking at binding cavities from the inside). This approach (called PSIM) was shown to support characterization of proteins of unknown function. Using PSIM combined with other data, we developed hybrid approaches for (2) ligand affinity prediction, (3) prediction of polypharmacology, and (4) bioactive small molecule pose prediction.
University of Minnesota, St. Paul, MN, BS, 1986, Biochemistry
University of Minnesota, Minneapolis, MN, BS, 1986, Computer Science
Carnegie Mellon University, Pittsburgh, PA, MS, 1989, Computer Science
Carnegie Mellon University, Pittsburgh, PA, PhD, 1991, Computer Science