ArticleJournal of chemical information and modeling2014
Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 112 citations in OpenAlex.
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- Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors.RSC medicinal chemistry · 2024Article
- Regulation of P-Glycoprotein during Oxidative Stress.Antioxidants (Basel, Switzerland) · 2024Review
- Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.Current drug research reviews · 2024Review
- Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.Current topics in medicinal chemistry · 2024Review
- Ensemble of structure and ligand-based classification models for hERG liability profiling.Frontiers in pharmacology · 2023Article
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- P-glycoprotein: new insights into structure, physiological function, regulation and alterations in disease.Heliyon · 2022Review
- Extended-ensemble docking to probe dynamic variation of ligand binding sites during large-scale structural changes of proteins.Chemical science · 2022Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization.
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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.