In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.
Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.
Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi 110 001, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India. Electronic address: kpsingh_52@yahoo.com.
Kunwar P
KP
Singh
Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi 110 001, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India.
Shikha
S
Gupta
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003663:Decision Trees; D004784:Environmental Monitoring; D016018:Least-Squares Analysis; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D018570:Risk Assessment; D013045:Species Specificity; D013269:Stochastic Processes; D013769:Tetrahymena pyriformis / Q000187:drug effects* / Q000254:growth & development; D014116:Toxicology / Q000379:methods*
Diverse chemicals; Ensemble learning models; Interspecies model; Molecular descriptors; Regulatory toxicology; Toxicity