Abstract
Because the health effects of many compounds are unknown, regulatory toxicology must often rely on the development of quantitative structure–activity relationship (QSAR) models to efficiently discover molecular initiating events (MIEs) in the adverse-outcome pathway (AOP) framework. However, the QSAR models used in numerous toxicity prediction studies are publicly unavailable, and thus, they are challenging to use in practical applications. Approaches that simultaneously identify the various toxic responses induced by a compound are also scarce. The present study develops Toxicity Predictor, a web application tool that comprehensively identifies potential MIEs. Using various chemicals in the Toxicology in the 21st Century (Tox21) 10K library, we identified potential endocrine-disrupting chemicals (EDCs) using a machine-learning approach. Based on the optimized three-dimensional (3D) molecular structures and XGBoost algorithm, we established molecular descriptors for QSAR models. Their predictive performances and applicability domain were evaluated and applied to Toxicity Predictor. The prediction performance of the constructed models matched that of the top model in the Tox21 Data Challenge 2014. These advanced prediction results for MIEs are freely available on the Internet.
Highlights
Quantitative structure–activity relationship (QSAR) analysis is a technique used to predict the physiological activity of low-molecular-weight compounds based on their molecular structure [1,2]
In the field of toxicology, QSAR methodology is used for quantitative structure–toxicity relationship (QSTR) modeling using complex toxicity and adverse effect onset mechanisms that are objective variables [3,4]
One missing but desirable functionality in the practical use of QSTR prediction is that resources, such as the toxicity prediction models, should be distributed as highly convenient public software
Summary
Quantitative structure–activity relationship (QSAR) analysis is a technique used to predict the physiological activity of low-molecular-weight compounds based on their molecular structure [1,2]. In the field of toxicology, QSAR methodology is used for quantitative structure–toxicity relationship (QSTR) modeling using complex toxicity and adverse effect onset mechanisms that are objective variables [3,4]. One missing but desirable functionality in the practical use of QSTR prediction is that resources, such as the toxicity prediction models, should be distributed as highly convenient public software. These toxicity prediction models should be published so that users can access QSTR prediction models for various toxicity targets [8,9,10]
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