ArticleEXCLI journal2015
Classification of P-glycoprotein-interacting compounds using machine learning methods.
Article in EXCLI journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Review
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- Effect of acid sphingomyelinase deficiency in type A Niemann-Pick disease on the transport of therapeutic nanocarriers across the blood-brain barrier.Drug delivery and translational research · 2023Article
- Antibacterial Properties and Computational Insights of Potent Novel Linezolid-Based Oxazolidinones.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Why Do Dietary Flavonoids Have a Promising Effect as Enhancers of Anthracyclines? Hydroxyl Substituents, Bioavailability and Biological Activity.International journal of molecular sciences · 2022Review
- Using Jupyter Notebooks for re-training machine learning models.Journal of cheminformatics · 2022Article
- Machine learning models for classification tasks related to drug safety.Molecular diversity · 2021Review
- Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides.Molecules (Basel, Switzerland) · 2020Review
- iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.International journal of molecular sciences · 2019Article
- Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.International journal of molecular sciences · 2019Article
- TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides.International journal of molecular sciences · 2019Article
- P-glycoprotein transporter in drug development.EXCLI journal · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 non-inhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance.
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Registered trials
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