Although genomic information provides many potential targets for drug discovery, the challenge is to convert this information into drugs that cure human disease. Workers in computer assisted molecular design and chemometrics have developed a number of techniques to aid this process. Examples include selecting diverse compounds for high throughput screening, designing universal or targeted combinatorial libraries, and using a variety of computational techniques to forecast binding affinitiy and bioavailability of compounds. This lecture will summarize recent advances in these areas.

Automation has produced the opportunity to test hundreds of thousands of compounds in a biological test. The challenge of the computational chemist is to select, from the 10180 possible drug-like molecules, those that are most attractive for screening, those that could form the basis of a medicinal chemistry program. Early work focused on selecting diverse compounds, leading to the question of how similar compounds must be in order for them to have similar biological activity. The issue, of course, is how to quantitate the similarity—which molecular descriptors and which similarity calculation provide the best result. More recent work has emphasized the consideration of physical properties to eliminate compounds, with the challenge of finding accurate and comprehensive methods to calculate these properties.