ArticleBioinformatics (Oxford, England)2024
PocketDTA: an advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.Interdisciplinary sciences, computational life sciences · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- A meta learning and task adaptive approach for drug target affinity prediction.Nature communications · 2026Article
- Deciphering DEL pocket patterns through contrastive learning.Nature communications · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- TriDTI: tri-modal representation learning with cross-modal alignment for drug-target interaction prediction.Briefings in bioinformatics · 2026Article
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug-target interaction prediction.Frontiers in chemistry · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- GraphBAN: An inductive graph-based approach for enhanced prediction of compound-protein interactions.Nature communications · 2025Article
- Dual modality feature fused neural network integrating binding site information for drug target affinity prediction.NPJ digital medicine · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
motivationAccurately predicting the drug-target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of 3D information, and poor interpretability.
resultsTo alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first 3 (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. In addition, it uses a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability. AVAILABILITY AND IMPLEMENTATION: Code and data are available at: https://github.com/zhaolongNCU/PocketDTA.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.