Plenary Lecture

Charting Chemical Space with Computers: Challenges and Opportunities for AI and Machine Learning

Monday, April 2, 2007 8:00am - 9:00am South Pacific Ballroom 3

Presenter: Pierre Baldi, PhD, Professor, Director Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, Department of Biological Chemistry, University of California, Irvine

Abstract:

Informatics and computers have not yet become as pervasive in chemistry as they have in physics and biology. Drawing analogies from bioinformatics, key ingredients for progress in chemoinformatics are the availability of large, annotated databases of compounds and reactions, data structures and algorithms to efficiently search these databases, and computational methods to predict the physical, chemical, and biological properties of new compounds and reactions. We will describe the development of: (1) a large public database of compounds and reactions (ChemDB); (2) machine learning kernel methods to predict molecular properties; and (3) the applications of these methods to drug screening/design problems and the identification of new drug leads against a major disease.

Biosketch:

Pierre Baldi is a Professor in the School of Information and Computer Sciences and the Department of Biological Chemistry at the University of California, Irvine and the Director of the UCI Institute for Genomics and Bioinformatics. He received a PhD in Mathematics in 1986 from the California Institute of Technology.

Dr. Baldi has held postdoctoral, faculty, and member of the technical staff positions at UCSD and Caltech, in the Division of Biology and the Jet Propulsion Laboratory. He was CEO of a startup company for a few years and joined UCI in 1999. He is the recipient of a 1993 Lew Allen Award at JPL and a Laurel Wilkening Faculty Innovation Award at UCI. Dr. Baldi's has published over 150 scientific articles and four book. Research in his group focuses on several areas at the intersection of the computational and life sciences, in particular the application of AI/statistical/machine learning methods to problems in bio and chemical informatics. Work in his group has resulted in several databases, software, and web servers (see: http://www.igb.uci.edu/servers/servers.html) that are in wide use. His main contributions include the development of Hidden Markov Models (HMMPro) for sequence analysis, recursive neural networks for de novo protein structure prediction (SCRATCH), Bayesian statistical methods for DNA microarray analysis (Cyber-T), informatics infrastructure for systems biology (SIGMOID) and, more recently, databases and tools in chemical informatics (ChemDB) for the prediction of molecular properties and applications in chemical synthesis, discovery, and drug design.