A fresh strategy in multi-target drug development, polypharmacology-regulating numerous targets with one or more drugs ( ) has attracted significant attention. This method mainly targets specific targets that particular medications can reasonably target ( ). Thanks to substantial developments in the field of in silico pharmacology, several practical approaches for exploring multi-target medicines have emerged. Among these methods are pharmacophore modelling, network pharmacology, molecular docking, multi-omics-based system biology, Multi-target Quantitative Structure-Activity Relationship (mt-QSAR), machine learning, and perturbation model combined with machine learning (PLMT ( )). Thus, it is expected that including different biological data and improving machine learning methods will help to optimise multi-target drug prediction models.