Integrating sequence and graph information for enhanced drug-target affinity prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s11432-022-3793-7.pdf
Reference5 articles.
1. Smietana K, Siatkowski M, Møller M. Trends in clinical success rates. Nat Rev Drug Discov, 2016, 15: 379–380
2. Zheng S J, Li Y J, Chen S, et al. Predicting drug-protein interaction using quasi-visual question answering system. Nat Mach Intell, 2020, 2: 134–140
3. Pandey M, Fernandez M, Gentile F, et al. The transformational role of GPU computing and deep learning in drug discovery. Nat Mach Intell, 2022, 4: 211–221
4. Yuan W N, Chen G X, Chen C Y C. FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. Briefings BioInf, 2022, 23: bbab506
5. Yang Z D, Zhong W H, Zhao L, et al. MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chem Sci, 2022, 13: 816–833
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