Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

中文翻译：

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深度学习以预测化学物质的毒性：一个简短的综述。

人类和野生动植物栖息于一个充满天然和合成化学物质的世界。令人震惊的是，由于传统毒性测试的局限性，仅有限数量的化学物质经历了全面的毒理学评估。高通量筛选测定法为常规毒性测试提供了更高的替代方法。在过去十年中，高通量生物测定技术的发展极大地增加了化学毒性数据量，从而将毒理学研究推向了“大数据”时代。但是，传统的数据分析方法无法有效地处理大量数据，这对毒理学家而言既是挑战，也是机遇。深度学习，一种利用深度神经网络（DNN）的机器学习方法，是使用这些新的大型数据集建立定量构效关系（QSAR）模型进行毒性预测的有效工具。在此小型回顾中，提供了有关DNN的简要技术背景，并回顾了使用DNN构建的化学毒性预测模型的当前状态。此外，总结了相关的毒性数据来源，讨论了可能的局限性，并给出了在化学毒性预测中利用DNN的观点。