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Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2
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Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2
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Journal Article
Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2
2026
Overview
YTHDC2, a unique YTH-domain-containing protein that recognizes N6-methyladenosine (m
6
A) on RNA, plays critical roles in diverse pathological processes and represents a promising therapeutic target. 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 IC
50
of 16.84 μM. Subsequent structural optimization of
H3
yields
DC2-C1
, a highly potent compound with an IC
50
of 0.168 μM against YTHDC2 and selectivity over other YTH-domain proteins. In cellular assays,
DC2-C1
effectively targets YTHDC2. Notably,
DC2-C1
treatment substantially reduces the expression levels of multiple target mRNAs of YTHDC2, leading to phenotypic suppression of related cells. Overall, this study highlights the great potential of deep learning in drug discovery and provides a promising lead compound for drug development targeting YTHDC2.
YTHDC2 is a promising therapeutic target, but lacks potent inhibitors. Yang et al. develop a deep learning-based molecule generator EPMolGen. Using this model, they discover a potent, selective, and cell-active small molecule inhibitor of YTHDC2.
