Dataset for Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2

Despite its potential, no potent small-molecule inhibitors have been reported to date. To bridge this gap, we develop EPMolGen, a deep learning-based molecular generative model that explicitly incorporates the electrostatic features of receptor proteins.

The model achieves state-of-the-art performance in dry-lab validations. Using EPMolGen, we identify H3, a YTHDC2 inhibitor with an IC50 of 16.84 μM. Subsequent structural optimization of H3 yields DC2-C1, a highly potent compound with an IC50 of 0.168 μM against YTHDC2 and selectivity over other YTH-domain proteins. In cellular assays, DC2-C1 effectively targets YTHDC2.