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Predictions of the ADMET Properties of Candidate Drug Molecules Utilizing Different QSAR/QSPR Modelling Approaches

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The integration of early ADMET (absorption, distribution, metabolism, excretion and toxicity) profiling, or simply prediction, of 'lead' molecules to speed-up the 'lead' selection further for phase-I trial without losing large amount of revenue. The ADMET profiling and prediction is mostly dependent of a number of molecular descriptors, for example, Lipinski's 'Rule of 5' (Ro5). Recently a large number of articles have been reporting that it possible to do some prediction of the ADMET properties using the structural features of the molecules, utilizing several and multiple approaches. One of the most important approaches is the QSAR/QSPR modelling of the data derived from their activity profiles and their different structural features (i.e., quantitative molecular descriptors).

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Keywords: ADMET; PSA; QSAR modeling; artificial neural network; drugability; inductive logic programming; logD; logP; machine learning; molecular descriptor; multiple linear regressions; support vector machine

Document Type: Research Article

Publication date: May 1, 2010

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  • Current Drug Metabolism aims to cover all the latest and outstanding developments in drug metabolism and disposition. The journal serves as an international forum for the publication of timely reviews in drug metabolism. Current Drug Metabolism is an essential journal for academic, clinical, government and pharmaceutical scientists who wish to be kept informed and up-to-date with the latest and most important developments. The journal covers the following areas:

    In vitro systems including CYP-450; enzyme induction and inhibition; drug-drug interactions and enzyme kinetics; pharmacokinetics, toxicokinetics, species scaling and extrapolations; P-glycoprotein and transport carriers; target organ toxicity and interindividual variability; drug metabolism and disposition studies; extrahepatic metabolism; phase I and phase II metabolism; recent developments for the identification of drug metabolites and adducts.
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