Predicting targeted polypharmacology for drug repositioning and multi-target drug discovery

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.