Machine learning prediction of acute toxicity with <i>in vivo</i> experiments on tetrazole derivatives.

ABSTRACT: This study investigates the application of machine learning techniques to predict the toxicity of tetrazole derivatives, aiding in the identification of environmental risks from chemical exposure. Utilizing LD₅₀ data sourced from the scientific literature and the ChemIDplus database, regression models were developed to forecast acute intraperitoneal toxicity in mice. A machine learning regression model for acute intraperitoneal toxicity in mice was constructed and validated on a test dataset, achieving high accuracy (R ² = 0.76 and MSE below 10^-4) and surpassing most of the comparable literature models. Molecular descriptors were computed via Mordred software to explore quantitative structure-activity relationships, and additionally, the model

SUBMITTER: Zarezin DP 

PROVIDER: S-EPMC12648568 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature