A precise comparison of molecular target prediction methods

Abstract

Small-molecule drug discovery has transitioned from traditional phenotypic screening to more precise target-based approaches, with an increased focus on understanding mechanisms of action (MoA) and target identification. With more research on off-target effects of approved drugs and the discovery of new therapeutic targets, revealing hidden polypharmacology can reduce both time and costs in drug discovery through off-target drug repurposing. However, despite the potential of in-silico target prediction, its reliability and consistency remain a challenge across different methods. This project systematically compares seven target prediction methods, including stand-alone codes and web servers (MolTarPred, PPB2, RF-QSAR, TargetNet, ChEMBL, CMTNN and SuperPred), using a shared benchmark dataset of FDA-approved drugs. We also explore model optimization strategies, such as high-confidence filtering, which reduces recall, making it less ideal for drug repurposing. Furthermore, for MolTarPred, Morgan fingerprints with Tanimoto scores, outperforms MACCS fingerprints with Dice scores. This analysis shows that MolTarPred is the most effective method. For practical applications, we introduce a programmatic pipeline for target prediction and MoA hypothesis generation. A case study on Fenofibric Acid shows its potential for drug repurposing as an THRB modulator for thyroid cancer treatment.