Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

Department of Bioengineering and Therapeutic Sciences, University of California , San Francisco, California 94143-0912, United States.
Journal of Medicinal Chemistry (Impact Factor: 5.45). 10/2012; 55(20):8926-42. DOI: 10.1021/jm301210j
Source: PubMed


Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for "synthesis." Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection.

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