ArticleActa pharmacologica Sinica2014
Multi-algorithm and multi-model based drug target prediction and web server.
Article in Acta pharmacologica Sinica, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The applications of deep learning algorithms on in silico druggable proteins identification.Journal of advanced research · 2022Article
- Exploration of Target Spaces in the Human Genome for Protein and Peptide Drugs.Genomics, proteomics & bioinformatics · 2022Article
- Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations.Methods in molecular biology (Clifton, N.J.) · 2017Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo develop a reliable computational approach for predicting potential drug targets based merely on protein sequence.
methodsWith drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets.
resultsTwenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access.
conclusionReliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.
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Registered trials
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