Integrating machine learning and protein–ligand interaction profiling for the discovery of METTL3 inhibitors

RNA modifications are critical in regulating gene expression and cell functions by affecting RNA stability, splicing, translation, and degradation. The catalytic core of N6-adenosine-methyltransferase catalytic subunit METTL3 has emerged as a key enzyme in tumorigenesis by enhancing the translation efficiency of oncogenic transcripts, which is a promising therapeutic target for cancers, including acute myeloid leukemia. In this study, we presented a novel METTL3 inhibitory bioactivity (pIC50) prediction model (ML3-mix-DPLIFE) by combining machine learning, protein–ligand docking, and protein–ligand interaction analysis, through encoding the conventional physicochemical properties, chemical fingerprint, and the docking-based protein–ligand interaction features (DPLIFE) with leveraging auto-stacking 6 algorithms. A feature selection algorithm further optimized the model (ML3-mix-DPLIFE-FS) and obtained a promising mean squared error (MSE) of 0.261 and a Pearson's correlation coefficient (CC) of 0.853 on an independent test dataset, and identified 8 residues critical for ligand interactions with METTL3. To further test the model, the pIC50s of recently reported inhibitors were predicted using the ML3-mix-DPLIFE-FS model, and a good MSE of 0.418 and CC of 0.727 were obtained. This innovative strategy seamlessly integrates machine learning prediction with structural biology insights and reveals a novel way to identify key protein–ligand interactions for further structural rational drug design.