Lipophilicity, as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D 7.4), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In this study, we developed several quantitative structure–property relationship (QSPR) models to predict log D 7.4 based on a large and structurally diverse data set. Eight popular machine learning algorithms were employed to build the prediction models with 43 molecular descriptors selected by a wrapper feature selection method. The results demonstrated that XGBoost yielded better prediction performance than any other single model (R T 2 = 0.906 and RMSET = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance (R T 2 = 0.922 and RMSET = 0.359). The robustness, reliability, and generalization ability of the models were strictly evaluated by the Y-randomization test and applicability domain analysis. Moreover, the group contribution model based on 110 atom types and the local models for different ionization states were also established and compared to the global models. The results demonstrated that the descriptor-based consensus model is superior to the group contribution method, and the local models have no advantage over the global models. Finally, matched molecular pair (MMP) analysis and descriptor importance analysis were performed to extract transformation rules and give some explanations related to log D 7.4. In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log D 7.4 in drug discovery.

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Naoxintong capsule (NXT) is a commercial medicinal product approved by the China Food and Drug Administration which is used in the treatment of stroke and coronary heart disease. However, the research on the composition and mechanism of NXT is still lacking. Our research aimed to identify the absorbable components, potential targets, and associated pathways of NXT with network pharmacology method. We explored the chemical compositions of NXT based on UPLC/Q-TOF-MS. Then, we used the five principles of drug absorption to identify absorbable ingredients. The databases of PharmMapper, Universal Protein, and the Molecule Annotation System were used to predict the main targets and related pathways. By the five principles of drug absorption as a judgment rule, we identified 63 compositions that could be absorbed in the blood in all 81 chemical compositions. Based on the constructed networks by the significant regulated 123 targets and 77 pathways, the main components that mediated the efficacy of NXT were organic acids, saponins, and tanshinones. Radix Astragali was the critical herbal medicine in NXT, which contained more active components than other herbs and regulated more targets and pathways. Our results showed that NXT had a therapeutic effect on heart diseases through the pattern “multiple components-multiple targets-multiple pathways.”

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