title: MiniMuDS: a new optimizer using knowledge-based potentials improves scoring of docking solutions.
abstract: In small molecule docking, the scoring and ranking of generated conformations is an important, though still not a completely resolved problem. Rescoring schemes often improve the quality of the obtained rankings. It is known that a local optimization is essential before a valid rescore value can be calculated. Here, we present a method that improves rescoring results obtained with the DrugScore function due to a new optimization technique. The method implements a more sophisticated search algorithm compared to the classic local optimization procedures used in this context. We validated the proposed method on a set of 192 protein-ligand complexes. Results show substantial improvements compared to original docking results with success rates increased by up to 10% for top scored solutions below 2 Å root-mean-square deviation to the native state and up to 18% increase below 1 Å, respectively.
authors.affiliation: Institute of Pharmaceutical Chemistry, Philipps-Universität Marburg, Marburg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Spitzmüller
authors.forename: Hans F G
authors.initials: HF
authors.lastname: Velec
authors.forename: Gerhard
authors.initials: G
authors.lastname: Klebe
mesh_terms: D000465:Algorithms; D018360:Crystallography, X-Ray; D015195:Drug Design; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D015203:Reproducibility of Results; D012984:Software
chemical_list: D008024:Ligands