title: Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery.
abstract: 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.
authors: State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610064, P.R. China.
authors: X
authors: X
authors: Liu
authors: F
authors: F
authors: Zhu
authors: X H
authors: XH
authors: Ma
authors: Z
authors: Z
authors: Shi
authors: S Y
authors: SY
authors: Yang
authors: Y Q
authors: YQ
authors: Wei
authors: Y Z
authors: YZ
authors: Chen
mesh_terms: D000818:Animals; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry; D010600:Pharmacology / Q000379:methods; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations