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. 2018 Nov 30;10(1):148-157.
doi: 10.1039/c8md00472b. eCollection 2019 Jan 1.

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness

Longfei Guan  1 , Hongbin Yang  1 , Yingchun Cai  1 , Lixia Sun  1 , Peiwen Di  1 , Weihua Li  1 , Guixia Liu  1 , Yun Tang  1
Affiliations

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness

Longfei Guan et al. Medchemcomm. .

Abstract

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 The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score.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.

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Figures

Fig. 1
Fig. 1. (A) Chemical space distribution of DrugBank, ChEMBL and WITHDRAWN. N represents the number of molecules in different data sets. Chemical space is defined by molecular weight (MW) as the X-axis, and Ghose–Crippen Log Kow (A log P) as the Y-axis. (B) Tanimoto similarity index for all chemicals used in this study; the color closer to red in the heat map means that the compounds are more similar and the color closer to dark blue means that the compounds have higher diversity.
Fig. 2
Fig. 2. Beneficial ratio of different models in the three data sets. Blue, orange and gray represent the three data sets, respectively.
Fig. 3
Fig. 3. Cumulative ADMET-score distribution for the three data sets: the ChEMBL data set (blue), the DrugBank data set (red), and the WITHDRAWN data set (green).
Fig. 4
Fig. 4. Correlation of drug-likeness between the ADMET-score and QED.
Fig. 5
Fig. 5. The distribution of QED values in the three data sets: (A) the probability density of the QED in the DrugBank data set, (B) the probability density of the QED in the WITHDRAWN data set, (C) the probability density of the QED in the ChEMBL data set, and (D) cumulative QED distribution of the three data sets.
Fig. 6
Fig. 6. Receiver operating characteristic (ROC) plots of the true positive rate (TPR; sensitivity) against the false positive rate (FPR; 1 – specificity) for the classification of compounds. ROC plot classification of compounds from DrugBank or ChEMBL using the QED (blue line) and ADMET-score (red line).
Fig. 7
Fig. 7. (A) Cumulative ADME-score distribution for the three data sets: the ChEMBL data set (blue), the DrugBank data set (red), and the WITHDRAWN data set (green). (B) Cumulative T-score distribution for the three data sets: the ChEMBL data set (blue), the DrugBank data set (red), and the WITHDRAWN data set (green).
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