title: Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
abstract: Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
authors.affiliation: Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa, Japan.
authors.forename: Kun-Yi
authors.initials: KY
authors.lastname: Hsin
authors.affiliation: The Systems Biology Institute, Minato, Tokyo, Japan ; Laboratory for Disease Systems Modeling, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
authors.forename: Samik
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa, Japan ; The Systems Biology Institute, Minato, Tokyo, Japan ; Laboratory for Disease Systems Modeling, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Kitano
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D055808:Drug Discovery; D053263:Gene Regulatory Networks / Q000187:drug effects*; D006801:Humans; D062105:Molecular Docking Simulation; D011485:Protein Binding; D060066:Protein Interaction Maps / Q000187:drug effects*; D047428:Protein Kinase Inhibitors / Q000494:pharmacology*; D011494:Protein Kinases / Q000737:chemistry* / Q000378:metabolism
chemical_list: D047428:Protein Kinase Inhibitors; D011494:Protein Kinases