ArticleJournal of natural science, biology, and medicine2011
A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine.
Article in Journal of natural science, biology, and medicine, 2011. 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.
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility.Foods (Basel, Switzerland) · 2024Article
- Article
- An "All-In-One" Pharmacophoric Architecture for the Discovery of Potential Broad-Spectrum Anti-Flavivirus Drugs.Applied biochemistry and biotechnology · 2018Article
- Chemometric Evaluation of THz Spectral Similarity for the Selection of Early Drug Candidates.Scientific reports · 2017Article
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Authors and funding
3 authors.
Funding
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Abstract
objectiveA computational model for predicting oral bioavailability is very important both in the early stage of drug discovery to select the promising compounds for further optimizations and in later stage to identify candidates for clinical trials. In present study, we propose a support vector machine (SVM)-based kernel learning approach carried out at a set of 511 chemically diverse compounds with known oral bioavailability values. MATERIAL AND
methodsFor each drug, 12 descriptors were calculated. The selection of optimal hyper-plane parameters was performed with 384 training set data and the prediction efficiency of proposed classifier was tested on 127 test set data.
resultsThe overall prediction efficiency for the test set came out to be 96.85%. Youden's index and Matthew correlation index were found to be 0.929 and 0.909, respectively. The area under receiver operating curve (ROC) was found to be 0.943 with standard error 0.0253.
conclusionThe prediction model suggests that while considering chemoinformatics approaches into account, SVM-based prediction of oral bioavailability can be a significantly important tool for drug development and discovery at a preliminary level.
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