ReviewBriefings in bioinformatics2019
Binding site matching in rational drug design: algorithms and applications.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Article
- Systems Biology-Driven Discovery of Host-Targeted Therapeutics for Oropouche Virus: Integrating Network Pharmacology, Molecular Docking, and Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 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
- Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?Biomacromolecules · 2024Review
- Comprehensive detection and characterization of human druggable pockets through binding site descriptors.Nature communications · 2024Article
- In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot.Nature communications · 2024Article
- Mce1R of Mycobacterium tuberculosis prefers long-chain fatty acids as specific ligands: a computational study.Molecular diversity · 2023Article
- HProteome-BSite: predicted binding sites and ligands in human 3D proteome.Nucleic acids research · 2023Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- Unveiling the molecular structure and role of RBBP4/7: implications for epigenetic regulation and cancer research.Frontiers in molecular biosciences · 2023Review
- Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.Journal of chemical information and modeling · 2022Article
- Machine learning based personalized drug response prediction for lung cancer patients.Scientific reports · 2022Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools.Pharmaceutics · 2022Review
- GraphSite: Ligand Binding Site Classification with Deep Graph Learning.Biomolecules · 2022Article
- Common Structural Pattern for Flecainide Binding in Atrial-Selective KPharmaceutics · 2022Article
- Dissecting and predicting different types of binding sites in nucleic acids based on structural information.Briefings in bioinformatics · 2022Article
- A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels.Frontiers in pharmacology · 2022Article
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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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.