Open AccessADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness
Author(s) -
Longfei Guan,
Hongbin Yang,
Yingchun Cai,
Lixia Sun,
Peiwen Di,
Weihua Li,
Guixia Liu
Publication year - 2019
Publication title -
medchemcomm
Language(s) - English
Resource type - Journals
eISSN - 2040-2511
pISSN - 2040-2503
DOI - 10.1039/c8md00472b
Subject(s) - drugbank , chembl , drug , in silico , drug development , drug discovery , function (biology) , pharmacology , computational biology , computer science , chemistry , bioinformatics , medicine , biology , evolutionary biology , biochemistry , gene
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 the 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 the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p -value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the 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.