title: Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches.
abstract: The hazardous dose of a chemical (HD50) is an emerging and acceptable test statistic for the safety/risk assessment of chemicals. Since it is derived using the experimental toxicity values of the chemical in several test species, it is highly cumbersome, time and resource intensive. In this study, three machine learning-based QSARs were established for predicting the HD50 of chemicals in warm-blooded species following the OECD guidelines. A data set comprising HD50 values of 957 chemicals was used to develop SDT, DTF and DTB QSAR models. The diversity in chemical structures and nonlinearity in the data were verified. Several validation coefficients were derived to test the predictive and generalization abilities of the constructed QSARs. The chi-path descriptors were identified as the most influential in three QSARs. The DTF and DTB performed relatively better than SDT model and yielded r(2) values of 0.928 and 0.959 between the measured and predicted HD50 values in the complete data set. Substructure alerts responsible for the toxicity of the chemicals were identified. The results suggest the appropriateness of the developed QSARs for reliably predicting the HD50 values of chemicals, and they can be used for screening of new chemicals for their safety/risk assessment for regulatory purposes.
authors.affiliation: a Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Lucknow , India.
authors.forename: S
authors.initials: S
authors.lastname: Gupta
authors.forename: N
authors.initials: N
authors.lastname: Basant
authors.forename: K P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D000818:Animals; D015386:Hazardous Substances / Q000737:chemistry / Q000633:toxicity*; D007928:Lethal Dose 50; D000069550:Machine Learning; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship
chemical_list: D015386:Hazardous Substances
keywords: hazardous dose; machine learning QSARs; structural alerts; structural diversity; warm-blooded species