MoleProLink-RL: geometric transport for domain-policy reinforcement learning in drug-target interaction prediction
Abstract Accurate drug-target interaction (DTI) prediction is vital to modern discovery workflows, yet models trained in one setting often underperform when chemotypes, protein families, or assay conditions shift. MoleProLink-RL addresses this challenge as a model-first solution that couples chemica...
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| Asıl Yazarlar: | , , , , , , , , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Nature Portfolio
2025-11-01
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| Seri Bilgileri: | npj Digital Medicine |
| Online Erişim: | https://doi.org/10.1038/s41746-025-02158-0 |
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