ArticleBMC structural biology2014
A simple method for finding a protein's ligand-binding pockets.
Article in BMC structural biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.Interdisciplinary sciences, computational life sciences · 2026Article
- Application of 3D atom pair map in an attention model for enhanced drug virtual screening.Journal of cheminformatics · 2025Article
- SP-DTI: subpocket-informed transformer for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- DrugReAlign: a multisource prompt framework for drug repurposing based on large language models.BMC biology · 2024Article
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
- SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions.Bioinformatics advances · 2023Article
- A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.BMC bioinformatics · 2022Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.Briefings in bioinformatics · 2022Article
- Computational study of pomegranate peel extract polyphenols as potential inhibitors of SARS-CoV-2 virus internalization.Molecular and cellular biochemistry · 2021Article
- DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation.PeerJ · 2020Article
- Binding site matching in rational drug design: algorithms and applications.Briefings in bioinformatics · 2019Review
- A Novel Geometry-Based Approach to Infer Protein Interface Similarity.Scientific reports · 2018Article
- POVME 3.0: Software for Mapping Binding Pocket Flexibility.Journal of chemical theory and computation · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis paper provides a simple and rapid method for a protein-clustering strategy. The basic idea implemented here is to use computational geometry methods to predict and characterize ligand-binding pockets of a given protein structure. In addition to geometrical characteristics of the protein structure, we consider some simple biochemical properties that help recognize the best candidates for pockets in a protein's active site.
resultsOur results are shown to produce good agreement with known empirical results.
conclusionsThe method presented in this paper is a low-cost rapid computational method that could be used to classify proteins and other biomolecules, and furthermore could be useful in reducing the cost and time of drug discovery.
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