Machine Learning Challenges in Chemoinformatics and Drug Screening and Design

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. More broadly, we will discuss some of the challenges and opportunities for computer science, AI, and machine learning in chemistry. Text Mining and Ontology Applications in Bioinformatics and GIS Shamkant B. Navathe College of Computing Georgia Institute of Technology