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

ABSTRACT: 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)

SUBMITTER: Huang WC 

PROVIDER: S-EPMC12540905 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature