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Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem

Lovrić, Mario; Malev, Olga; Klobučar, Goran; Kern, Roman; Liu, Jay; Lučić, Bono (2021) Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem. Molecules, 26 (6). ISSN 1420-3049

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The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure–activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew’s correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations

Item Type: Article
Uncontrolled Keywords: predictive QSAR ; toxicity ; ToxCast ; zebrafish embryo ; rdkit ; structural descriptors ; structural fingerprints ; machine learning ; imbalanced classification ; aquatic toxicology
Subjects: NATURAL SCIENCES > Biology
Divisions: Division for Marine and Enviromental Research
NMR Center
Depositing User: Bono Lučić
Date Deposited: 25 Mar 2021 12:24
DOI: 10.3390/molecules26061617

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