Machine learning-driven drug discovery for the management of TNBC: focus on IDO1 and TDO targets.

Abstract

Tryptophan catabolism through the kynurenine pathway produces the oncometabolite kynurenine, which is strongly implicated in cancers such as triple-negative breast cancer (TNBC). The enzymes indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-dioxygenase (TDO) drive this pathway and promote an immunosuppressive tumour microenvironment, making them an attractive therapeutic target. However, no approved drug currently inhibits both enzymes simultaneously. In this study, we employed a machine learning (ML)-driven virtual screening pipeline to identify potent dual IDO1 and TDO inhibitors. Initially, an in-house ML classification model was developed using IC₅₀ values from 1,037 distinct dual inhibitors sourced from the ChEMBL and BindingDB databases. Among the various models evaluated, the eXtreme Gradient Boosting with Random Forest (XGBRF) classifier achieved the highest performance (95% accuracy) and was selected to screen the MEGxp database. Subsequent molecular docking, MM-GBSA calculations, rescoring, and ADMET profiling identified two promising candidates, NP000319 and NP003833. Both compounds also showed predicted anticancer potential against MDA-MB-231 TNBC cells. Furthermore, the stability of the protein-ligand complexes was confirmed through 100 ns molecular dynamics simulations. Overall, the study highlights the value of ML-driven dual-inhibition strategies and provides strong leads for future experimental validation and potential therapeutic development for TNBC.