ArticleScientific reports2020
Improving detection of protein-ligand binding sites with 3D segmentation.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.Journal of chemical theory and computation · 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
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.Journal of cheminformatics · 2025Article
- Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization.Scientific reports · 2025Article
- Evaluation of Small-Molecule Binding Site Prediction Methods on Membrane-Embedded Protein Interfaces.Journal of chemical information and modeling · 2025Article
- InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.Nucleic acids research · 2025Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- A Survey of Deep Learning Methods and Tools for Protein Binding Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Enhancing substrate specificity of microbial transglutaminase for precise nanobody labeling.Synthetic and systems biotechnology · 2025Article
- Machine learning approaches for predicting protein-ligand binding sites from sequence data.Frontiers in bioinformatics · 2025Review
- Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?Combinatorial chemistry & high throughput screening · 2025Review
- SGPocket: A New Graph Convolutional Neural Network for Ligand-Protein Binding Site Prediction.Current medicinal chemistry · 2025Article
- Repurposing FDA-Approved Drugs Against Potential Drug Targets Involved in Brain Inflammation Contributing to Alzheimer's Disease.Targets (Basel) · 2024Article
- Comparative evaluation of methods for the prediction of protein-ligand binding sites.Journal of cheminformatics · 2024Article
- Structure and dynamics in drug discovery.npj drug discovery · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty.
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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.