ArticleJournal of cheminformatics2021
PUResNet: prediction of protein-ligand binding sites using deep residual neural network.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 152 citations in OpenAlex.
- Can Cavity Prediction Algorithms Help in Docking Experiments?Journal of computational chemistry · 2026Article
- Article
- 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
- Unified protein-small molecule graph neural networks for binding site prediction.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- SwinSite: 3D Structure-Based Prediction of Protein-Ligand Binding Sites Using a Combined Vision Transformer and Convolution Model.Journal of chemical information and modeling · 2026Article
- AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.Nature methods · 2026Article
- Designing de novo TIM barrels: insights into stabilization, diversification, and functionalization strategies.Biochemical Society transactions · 2026Review
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- CAPIM: Catalytic activity and site prediction and analysis tool in multimer proteins.Protein science : a publication of the Protein Society · 2025Article
- Computational Analysis of Plasmodium falciparum DNA Damage Inducible Protein 1 (PfDdi1): Insights into Binding of Artemisinin and its Derivatives and Implications for Antimalarial Drug Design.Cell biochemistry and biophysics · 2025Article
- Leveraging large language models for literature-driven prioritization of protein binding pockets.Bioinformatics (Oxford, England) · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- Evaluation of Small-Molecule Binding Site Prediction Methods on Membrane-Embedded Protein Interfaces.Journal of chemical information and modeling · 2025Article
- PrankWeb 4: a modular web server for protein-ligand binding site prediction and downstream analysis.Nucleic acids research · 2025Article
- Article
- AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.ACS omega · 2025Article
- Structures of Native Doublet Microtubules from Trichomonas vaginalis Reveal Parasite-Specific Proteins.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPredicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.
resultsIn this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.
Indexed as
Identifiers
What OpenQuestion holds
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.