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Dr. Ajay Jain received a PhD in Computer Science from Carnegie Mellon University in 1991 following BS degrees in Biochemistry and in Computer Science from the University of Minnesota in 1986. After a time in the defense industry working on machine vision and pattern recognition, he spent several years in the biopharmaceutical industry involved in a number of biotech start-up ventures, where his research focused on computer-aided drug design. Dr. Jain joined UCSF in 1999. He is currently appointed as Professor in the CancerResearch Institute, with additional appointments in the Departments of Biopharmaceutical Sciences and Laboratory Medicine. Fundamentally, the paradigm of predictive computational modeling encompasses all of the work in the Jain Lab. This ranges from modeling protein ligand interactions to modeling the relationship between variation in quantifiable molecularspecies to the behavior of complex biological systems. The primary areas of research in the lab are: 1) computational methods for structure-based drug design, 2) computational approaches for modeling human transcription and biological network structure, and 3) computational and statistical methods to derive quantitative conclusions from data gathered with high-throughput biological measurement technologies. All of the approaches share their roots in the use of empirically derived scoring functions and compute-intensive search, optimization, and enumeration.
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Drug Target Modeling: Ligands Tell Us More Than We Think
Ajay N. Jain, Ph.D., Professor, Cancer Research Institute and Depts. of Biopharmaceutical Sciences and Laboratory Medicine; Director, Informatics Core, UCSF Cancer Center
There are just over 1000 small molecule therapeutics approved for human use in the United States. Systematic annotation of their primary targets reveals that over 700 of these modulate approximately 85 biological targets. The results of multiple analyses, based exclusively on ligand-focused modeling, will be discussed. Drug/drug similarities and target/target similarities were computed on the basis of three-dimensional ligand structures. Drug pairs sharing a target had significantly higher similarity than drug pairs sharing no target. Also, target pairs with no overlap in annotated drug specificity shared lower similarity than target pairs with increasing overlap. Clustering analysis suggested that side effects and drug-druginteractions might be revealed by modeling many targets. Ligand-based models of diverse targets were constructed and tested in virtual screening protocols. Excellent enrichment was possible against backgrounds of screening molecules. More interesting, however, was that by crossing all drugs against all targets, it becomes possible to identify a number of known side effects, drug specificities, and drug-drug interactions that have a rational basis in molecular structure.
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