Computational chemistry-driven decision making in lead generation.

Abstract

Novel starting points for drug discovery projects are generally found either by screening large collections of compounds or smaller more-focused libraries. Ideally, hundreds or even thousands of actives are initially found, and these need to be reduced to a handful of promising lead series. In several sequential steps, many actives are dropped and only some are followed up. Computational chemistry tools are used in this context to predict properties, cluster hits, design focused libraries and search for close analogues to explore the potential of hit series. At the end of hit-to-lead, the project must commit to one, or preferably a few, lead series that will be refined during lead optimization and hopefully produce a drug candidate. Striving for the best possible decision is crucial because choosing the wrong series is a costly one-way street.