Issue 8, 2024

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

Abstract

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schür et al., Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on the molecular representations of the chemicals, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to consistently predict the toxicity of individual chemicals accurately enough. Predictions for single chemicals are mostly influenced by a few chemical properties while taxonomic traits are not captured sufficiently by the models. We discuss technical and conceptual improvements for these challenges to enhance the suitability of in silico methods to environmental hazard assessment. Accordingly, this work showcases state-of-the-art models and contributes to the ongoing discussion on regulatory integration.

Graphical abstract: Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

Supplementary files

Article information

Article type
Paper
Submitted
05 Marts 2024
Accepted
24 Maijs 2024
First published
03 Jūn. 2024
This article is Open Access
Creative Commons BY license

Environ. Sci.: Adv., 2024,3, 1124-1138

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

L. Gasser, C. Schür, F. Perez-Cruz, K. Schirmer and M. Baity-Jesi, Environ. Sci.: Adv., 2024, 3, 1124 DOI: 10.1039/D4VA00072B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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