Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials

• URL: http://arxiv.org/abs/2510.07853v1
• Date: Thu, 09 Oct 2025 06:51:12 GMT
• Title: Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials
• Authors: Thomas Lautenschlager, Nils Friederich, Angelo Jovin Yamachui Sitcheu, Katja Nau, Gaëlle Hayot, Thomas Dickmeis, Ralf Mikut,
• Abstract summary: We demonstrate how representations learned via self-supervised learning can effectively identify toxicant-induced changes.<n>Our analysis shows that the learned representations using self-supervised learning are suitable for effectively distinguishing between the modes-of-action of different compounds.
• Score: 1.2197883665266451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-throughput toxicity testing offers a fast and cost-effective way to test large amounts of compounds. A key component for such systems is the automated evaluation via machine learning models. In this paper, we address critical challenges in this domain and demonstrate how representations learned via self-supervised learning can effectively identify toxicant-induced changes. We provide a proof-of-concept that utilizes the publicly available EmbryoNet dataset, which contains ten zebrafish embryo phenotypes elicited by various chemical compounds targeting different processes in early embryonic development. Our analysis shows that the learned representations using self-supervised learning are suitable for effectively distinguishing between the modes-of-action of different compounds. Finally, we discuss the integration of machine learning models in a physical toxicity testing device in the context of the TOXBOX project.

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