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ADMET-score - A Comprehensive Scoring Function for Evaluation of Chemical Drug-likeness


Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in ADMET-score was determined by three parameters: the accuracy rate of model, the importance of the endpoint in the process of pharmacokinetics, and the beneficial distribution in oral drugs. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of ADMET-score. The indices of arithmetic mean and p-value showed that ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.

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Publication details

The article was received on 20 Sep 2018, accepted on 29 Nov 2018 and first published on 30 Nov 2018

Article type: Research Article
DOI: 10.1039/C8MD00472B
Citation: Med. Chem. Commun., 2018, Accepted Manuscript
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    ADMET-score - A Comprehensive Scoring Function for Evaluation of Chemical Drug-likeness

    L. Guan, H. Yang, Y. Cai, L. Sun, P. Di, W. Li, G. Liu and Y. Tang, Med. Chem. Commun., 2018, Accepted Manuscript , DOI: 10.1039/C8MD00472B

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