ArticleBioinformatics (Oxford, England)2023
CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 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
- BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.Journal of chemical information and modeling · 2026Article
- Advances in computational prediction of RNA-small molecule binding affinity.Journal of computer-aided molecular design · 2026Review
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- A geometric graph-based deep learning model for drug-target affinity prediction.BMC bioinformatics · 2025Article
- Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.Journal of chemical information and modeling · 2025Article
- EM-PLA: environment-aware heterogeneous graph-based multimodal protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.BMC biology · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity.BMC bioinformatics · 2025Article
- Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data.Communications chemistry · 2025Article
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
- Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.PloS one · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
motivationAccurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.
resultsIn this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications. AVAILABILITY AND IMPLEMENTATION: The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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