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

By Kun-Yi Hsin, Samik Ghosh, Hiroaki Kitano. PLoS ONE, 2013

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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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/57f6c958eeb94e96e8a8817671033e4c954e179f

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