Multispecies QSAR Modeling for Predicting the Aquatic Toxicity of Diverse Organic Chemicals for Regulatory Toxicology

The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R-2) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.