X. Liu, F. Zhu, X. H. Ma, Z. Shi, S. Y. Yang, Y. Q. Wei and Y. Z. Chen Pages 1646 - 1661 ( 16 )
Prediction of polypharmacology of known drugs and new molecules against selected multiple targets is highly useful for finding new therapeutic applications of existing drugs (drug repositioning) and for discovering multi-target drugs with improved therapeutic efficacies by collective regulations of primary therapeutic targets, compensatory signalling and drug resistance mechanisms. In this review, we describe recent progresses in exploration of in-silico methods for predicting polypharmacology of known drugs and new molecules by means of structure-based (molecular docking, binding- site structural similarity, receptor-based pharmacophore searching), expression-based (expression profile/signature similarity disease-drug and drug-drug networks), ligand-based (similarity searching, side-effect similarity, QSAR, machine learning), and fragment-based approaches that have shown promising potential in facilitating drug repositioning and the discovery of multi-target drugs.
Computer aided drug design, drug discovery, drug repositioning, gene expression, multi-target, network pharmacology, systems pharmacology, virtual screening
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