Systematic Modeling of log Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), 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  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 ( = 0.906 and RMSE = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance ( = 0.922 and RMSE = 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 . In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log  in drug discovery.