Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

• DOI: 10.1371/journal.pone.0083922
• Publication Date: 2013-12-31T22:04:35Z
• Authors: Kun-Yi Hsin; Samik Ghosh; Hiroaki Kitano
• Keywords: 0303 health sciences; Science; Q; R; Computational Biology; Molecular Docking Simulation; 03 medical and health sciences; Artificial Intelligence; Drug Discovery; Medicine; Humans; Gene Regulatory Networks; Protein Interaction Maps; Protein Kinase Inhibitors; Protein Kinases; Research Article; Protein Binding

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.