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.
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Figure: Illustration of a systems docking simulation. It is
mainly used to dock a small molecule to a protein structure and to evaluate its
potential to bind.
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Docking
simulation, a screening method to rapidly assess a test compound's binding
activity, is especially helpful in early stage pharmacology studies. We
developed a docking method using machine learning approach, which integrates
features of structure-based rational drug design and QSAR into the learning
models to enhance performance in molecular recognition. Docking simulation conducted
by machine learning systems A + B provides improved reliability in predicting
binding potentials and the capability of identifying potential targets. To
achieve more accurate prediction, further integration of other computer-aided
technology is feasible, such as the application of molecular dynamics (MD)
after docking. Together with a curated signaling map, the network-based
screening approach is able to comprehensively characterize the underlying
mechanism of a drug candidate's activity and also to interpret the cascade
effects of modulated targets. Adverse side effects constitute an enormous cost
in drug development. By applying network-based screening, drug developers can
reduce the possibility of marketing a drug with unfavorable drug-target interactions.
On the other hand, it also provides an opportunity to rationally optimize
inhibitor polypharmacology for treating complex diseases, such as cancer,
neurodegenerative disorders, cardiovascular disease, and metabolic syndromes.
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