Ajay N Jain is appointed as Professor in the Department of Bioengineering and Therapeutic Sciences at UCSF. He received his PhD in Computer Science in 1991 from Carnegie Mellon University. His undergraduate training was at the University of Minnesota, resulting in BS degrees in Biochemistry and in Computer Science. He spent a number of years in the defense industry during the Cold War prior to participating in a series of biotechnology start-up companies in the San Francisco Bay Area. He is known for work in computer-aided drug design, including Compass, Hammerheard, and the Surflex family of tools.


There are numerous widely available methods for small-molecule molecular docking, many fewer for computing 3D small molecule similarity, and fewer still for modeling protein binding site similarity. But data that are relevant to many problems faced in modern drug discovery generally come from ligand activity information as well as biophysical experiments. Problems including binding mode prediction, off-target identification, lead identification through virtual screening, and predictive modeling of binding affinities can all benefit from careful harmonization of methods that exploit data from multiple perspectives. Here, general methods for combining information will be presented, with specific applications to binding-mode prediction and off-target prediction.