ArticleInternational journal of molecular sciences2022
CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Agrimoniin ameliorates intrapulmonary angiogenesis and improves hypoxemia in hepatopulmonary syndrome via PGC-1α activation and glycolysis down-regulation.Genes & diseases · 2026Article
- [A protein-ligand binding affinity prediction model integrating multi-scale interaction features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Review
- GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.Molecular diversity · 2025Article
- GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity.BMC genomics · 2025Article
- Nature's cryptographic codebreakerFrontiers in microbiology · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.Cancers · 2024Article
- Techniques and Strategies in Drug Design and Discovery.International journal of molecular sciences · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
- 3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.RSC advances · 2023Article
- Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Design and Prediction of Aptamers Assisted by In Silico Methods.Biomedicines · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug-target affinity is crucial. The proposed model, the prediction of drug-target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug-target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.
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