ArticleBiomolecules2022
GraphSite: Ligand Binding Site Classification with Deep Graph Learning.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- DSC-bsite: a dynamic-static collaborative multimodal graph learning method for protein-small molecule binding site prediction.Molecular diversity · 2026Article
- Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Caveat emptor: predicting and modeling protein-DNA recognition and binding via machine-learning computational approaches.Nucleic acids research · 2026Review
- PARP1 inhibitors discovery: innovative screening strategies incorporating machine learning and fragment replacement techniques.Molecular diversity · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation.Briefings in bioinformatics · 2025Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- NABhClassifier Server: A Tool for the Identification of Helical Nucleic Acid-Binding Sequences in Proteins.Journal of chemical information and modeling · 2025Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model.Journal of cheminformatics · 2024Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.Bioinformatics (Oxford, England) · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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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.