In silico prediction of chemical neurotoxicity using machine learning.

ABSTRACT: Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity ([Formula: see text] = 0.784). In addition, we get the applicability domain of the models by calculating the standard devia

SUBMITTER: Jiang C 

PROVIDER: S-EPMC7329181 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature