用自监督学习识别化学物毒性作用机制，助力高通量安全测试
Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials
- 基于自监督学习提取表型特征，自动识别化合物毒性模式
- 在十类斑马鱼胚胎表型数据上实现不同毒作用机制的有效区分
- 为TOXBOX毒性检测设备提供可集成的机器学习方案
高通量毒性测试为快速低成本评估大量化合物提供了可能，其核心在于机器学习模型的自动化评估。本文针对该领域关键挑战，展示通过自监督学习获得的表征能有效识别毒物诱导的生物学变化。我们以公开的EmbryoNet数据集为验证，该数据集包含多种化学物质引发的十类斑马鱼胚胎表型，分别对应早期发育中的不同作用靶点。分析表明，自监督学习所得表示能有效区分不同化合物的作用机制。最后，讨论了将此类机器学习模型集成至物理毒性检测装置中的可行性，结合TOXBOX项目展望实际应用前景。
原文摘要 · Abstract (English)
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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