ArticleBriefings in bioinformatics2023
Predicting drug-target binding affinity with cross-scale graph contrastive learning.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- [Prediction and verification of therapeutic drugs for triple-negative breast cancer using a knowledge graph-based drug repurposing model].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification.Genomics, proteomics & bioinformatics · 2026Article
- A meta learning and task adaptive approach for drug target affinity prediction.Nature communications · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- CMMSCL-DPI: cross-modal multi-structural contrastive learning for predicting drug-protein interactions.BMC biology · 2026Article
- MAKA-Map: Real-Valued Distance Prediction for Protein Folding Mechanisms via a Hybrid Neural Framework Integrating the Mamba and Kolmogorov-Arnold Networks.Biomolecules · 2026Article
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein-protein interaction site identification.BMC bioinformatics · 2025Article
- Accurate prediction of drug-protein interactions by maintaining the original topological relationships among embeddings.BMC biology · 2025Article
- MixingDTA: improved drug-target affinity prediction by extending mixup with guilt-by-association.Bioinformatics (Oxford, England) · 2025Article
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- DTI-LM: language model powered drug-target interaction prediction.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.
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