ArticlePloS one2013
A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 44 citations in OpenAlex.
- Article
- Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.Molecular diversity · 2022Article
- Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food-Drug Interactions (FDI).International journal of molecular sciences · 2022Review
- Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier.Journal of agricultural and food chemistry · 2022Article
- Unsupervised Representation Learning for Proteochemometric Modeling.International journal of molecular sciences · 2021Article
- QSAR without borders.Chemical Society reviews · 2020Review
- Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database.ACS medicinal chemistry letters · 2019Article
- Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.Current drug metabolism · 2017Review
- XMetDB: an open access database for xenobiotic metabolism.Journal of cheminformatics · 2016Article
- Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.Molecular diversity · 2015Article
- Proteochemometric model for predicting the inhibition of penicillin-binding proteins.Journal of computer-aided molecular design · 2015Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.
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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.