Lu, Jing published the artcileEstimation of elimination half-lives of organic chemicals in humans using gradient boosting machine, Name: Diatrizoic Acid, the main research area is organic chem pharmacokinetics simulation gradient boosting machine; Applicability domain; Consensus model; Elimination half-life; Gradient boosting machine.
Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The exptl. evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R2 = 0.820 and RMSE = 0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R2 = 0.738 and RMSE = 0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R2 = 0.723 and RMSE = 0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled “”System Genetics”” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
Biochimica et Biophysica Acta, General Subjects published new progress about Drug metabolism. 117-96-4 belongs to class isoquinoline, name is Diatrizoic Acid, and the molecular formula is C11H9I3N2O4, Name: Diatrizoic Acid.
Referemce:
Isoquinoline – Wikipedia,
Isoquinoline | C9H7N – PubChem