title: kNNsim: k-nearest neighbors similarity with genetic algorithm features optimization enhances the prediction of activity classes for small molecules.
abstract: Protein targets specificity classification is an important step in computational drug development and design efforts. The enhanced classification models of small chemical molecules enable the rapid scanning of large compounds databases. Here, we present the k-nearest neighbors with genetic algorithm feature optimization approach for selection of small molecule protein inhibitors. The method is trained on selected, diverse activity classes of the MDL drug data report (MDDR) with ligands described using simple atom pairs two dimensional chemical descriptors. The accuracy of inhibitors identification is presented in confusion tables with calculated recall and precision values. The precision for selected types of targets exceeded 70%, and the recall reaches 40%. As a consequence, the method can be easily applied to large commercial compounds collections in a drug development campaign in order to significantly reduce the number of ligands for further costly experimental validation.
authors.affiliation: Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a, 02-106 Warsaw, Poland. D.Plewczynski@icm.edu.pl
authors.forename: Dariusz
authors.initials: D
authors.lastname: Plewczynski
mesh_terms: D000465:Algorithms; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D008024:Ligands; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000737:chemistry; D011485:Protein Binding; D011506:Proteins / Q000037:antagonists & inhibitors / Q000737:chemistry / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D012984:Software
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins