ArticleBMC bioinformatics2021
Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.Molecular diversity · 2026Article
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- A geometric graph-based deep learning model for drug-target affinity prediction.BMC bioinformatics · 2025Article
- Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.Nature communications · 2025Article
- PLAIG: Protein-Ligand Binding Affinity Prediction Using a Novel Interaction-Based Graph Neural Network Framework.ACS bio & med chem Au · 2025Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Resolving data bias improves generalization in binding affinity prediction.Nature machine intelligence · 2025Article
- Targeting serotonin receptors with phytochemicals - an in-silico study.Scientific reports · 2024Article
- Synchronized motion of interface residues for evaluating protein-RNA complex binding affinity: Application to aptamer-mediated inhibition of TDP-43 aggregates.Protein science : a publication of the Protein Society · 2024Article
- Discovery of Indole-Thiourea Derivatives as Tyrosinase Inhibitors: Synthesis, Biological Evaluation, Kinetic Studies, and In Silico Analysis.International journal of molecular sciences · 2024Article
- MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.BMC biology · 2024Article
- Distance plus attention for binding affinity prediction.Journal of cheminformatics · 2024Article
- From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.Life (Basel, Switzerland) · 2024Review
- Exploring LDLR-APOB Interactions in Familial Hypercholesterolemia in the Vietnamese Population: A Protein-Protein Docking Approach.Bioinformatics and biology insights · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundAccurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complex is ongoing.
resultsIn this study, we proposed a deep-neural network model to improve the prediction accuracy of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein-ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets.
conclusionsWe confirmed that an attention mechanism can capture the binding sites in a protein-ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .
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