ArticleBioinformatics (Oxford, England)2016
Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Predicting binding sites from unbound versus bound protein structures.Scientific reports · 2020Article
- Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPredictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.
resultsWe developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. AVAILABILITY AND IMPLEMENTATION: The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. CONTACT: shimizu@bi.a.u-tokyo.ac.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.