From knowledge-based potentials to combinatorial lead design in silico.

Bartosz A Grzybowski; Alexey V Ishchenko; Jun Shimada; Eugene I Shakhnovich

doi:10.1021/ar970146b

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From knowledge-based potentials to combinatorial lead design in silico.

Bartosz A Grzybowski·Alexey V Ishchenko·Jun Shimada·Eugene I Shakhnovich

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Abstract

Computational methods are becoming increasingly used in the drug discovery process. In this Account, we review a novel computational method for lead discovery. This method, called CombiSMoG for "combinatorial small molecule growth", is based on two components: a fast and accurate knowledge-based scoring function used to predict binding affinities of protein-ligand complexes, and a Monte Carlo combinatorial growth algorithm that generates large numbers of low-free-energy ligands in the binding site of a protein. We illustrate the advantages of the method by describing its application in the design of picomolar inhibitors for human carbonic anhydrase.

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Algorithms Binding Sites Combinatorial Chemistry Techniques Drug Design Models, Molecular Proteins Journal Article Review

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Bartosz A Grzybowski, Alexey V Ishchenko, Jun Shimada, Eugene I Shakhnovich. 2002. From knowledge-based potentials to combinatorial lead design in silico.. https://doi.org/10.1021/ar970146b

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