ArticleJournal of chemical information and modeling2022
Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.
Article in Journal of chemical information and modeling, 2022. 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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Who cites it
10 citing papers in PubMed.
- From Dynamics to Diagnosis and Therapy: A Multiscale Computational Framework for MALT1-Targeted Cancer Theranostics.Journal of chemical information and modeling · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases fromJournal of chemical information and modeling · 2025Article
- Comprehensive detection and characterization of human druggable pockets through binding site descriptors.Nature communications · 2024Article
- Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning.Journal of chemical information and modeling · 2024Article
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
- Graph embedding and geometric deep learning relevance to network biology and structural chemistry.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.
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Registered trials
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