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.
Highlights
Drugs may interact with numerous molecules in the human body
We developed a novel scoring approach employing two machine learning systems, which were embedded as a part of a pipeline implementing a network-based screening approach that integrates curated signaling networks, bioinformatics databases, and molecular docking simulation to comprehensively and rapidly evaluate potential binding affinities of given drugs against proteins involved in a signaling network
The first machine learning system we employed (A) was a rescoring function developed to assess binding modes generated by docking tools and to rank them
Summary
Drugs may interact with numerous molecules in the human body. Approximately 35% of known drugs or drug leads present multi-target activity [1]. Even when a drug is claimed to have high selectivity, it probably binds to proteins that are not identified as targets. Such unexpected off-target interactions may result in adverse reactions, which increase therapeutic risks and negatively impact drug development. An example of this is the cardiotoxicity of the tyrosine kinase inhibitor Sunitinib [2]. For example, was initially developed as a RAF kinase inhibitor, but its therapeutic contribution in curing renal and hepatocellular cancers was later ascribed to its inhibition of VEGFR2 and PDGFR, and probably other targets as well [7].
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