title: From knowledge-based potentials to combinatorial lead design in silico.
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.
authors.affiliation: Concurrent Pharmaceuticals, 1 Broadway, 14th floor, Cambridge, Massachusetts 02142, USA.
authors.forename: Bartosz A
authors.initials: BA
authors.lastname: Grzybowski
authors.forename: Alexey V
authors.initials: AV
authors.lastname: Ishchenko
authors.forename: Jun
authors.initials: J
authors.lastname: Shimada
authors.forename: Eugene I
authors.initials: EI
authors.lastname: Shakhnovich
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D020650:Combinatorial Chemistry Techniques; D015195:Drug Design; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry
chemical_list: D011506:Proteins