ArticleNature communications2025
LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Emerging contaminants follow class-specific mechanistic regimes in sediment-water partitioning.Environmental science and ecotechnology · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.Briefings in bioinformatics · 2026Article
- RGTBind: RBF-gate graph transformer with spatially biased attention for protein-DNA binding-site prediction.Journal of molecular modeling · 2026Article
- From Genome Inspection to Precision Agrochemicals: A Structure-Based Antivirulence Roadmap for Sustainable Crop Protection againstJournal of agricultural and food chemistry · 2026Review
- CholBindNet as an interpretable neural network for cholesterol-binding site classification.Communications chemistry · 2026Article
- CholBindNet: Interpretable Neural Networks for Cholesterol Binding Site Prediction.bioRxiv : the preprint server for biology · 2026Article
- Computational insights for target prioritization and pesticide effects on intrauterine growth restriction (IUGR).Bioinformation · 2026Article
- DTBind: A Mechanism-Driven Deep Learning Framework for Accurate Prediction of Drug-Target Molecular Recognition.Research (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Protein-ligand interactions are crucial for understanding various biological processes and drug discovery and design. However, experimental methods are costly; single-ligand-oriented methods are tailored to specific ligands; multi-ligand-oriented methods are constrained by the lack of ligand encoding. In this study, we propose a structure-based method called LABind, designed to predict binding sites for small molecules and ions in a ligand-aware manner. LABind utilizes a graph transformer to capture binding patterns within the local spatial context of proteins, and incorporates a cross-attention mechanism to learn the distinct binding characteristics between proteins and ligands. Experimental results on three benchmark datasets demonstrate both the effectiveness of LABind and its ability to generalize to unseen ligands. Further analysis validates that LABind can effectively integrate ligand information to predict binding sites. Additionally, the application of LABind is extended to binding site center localization, sequence-based methods, and molecular docking tasks.
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Registered trials
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.