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

TLDR

Increased availability of bioinformatics resources is creating opportunities for network pharmacology to predict drug effects and toxicity from multi‑target interactions. The study presents a high‑precision computational prediction approach that combines two machine‑learning systems with multiple docking tools to assess binding potentials of a test compound against proteins in a complex molecular network. The approach employs one machine‑learning system as a re‑scoring function for docking‑generated binding modes and a second system as a binding‑mode selection function to identify the most predictive mode. Benchmark validations and a case study demonstrate that this method surpasses other techniques in prediction reliability and identifies primary or off‑targets of kinase inhibitors, facilitating comprehensive drug safety assessment through network maps.

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.