Advancing Computational Toxicology by Interpretable Machine Learning,Environmental Science & Technology

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Advancing Computational Toxicology by Interpretable Machine Learning
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2023-05-24 , DOI: 10.1021/acs.est.3c00653
Xuelian Jia ₁ , Tong Wang ₁ , Hao Zhu ₁

Affiliation

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are “black boxes” in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

中文翻译：

通过可解释的机器学习推进计算毒理学

药物、消费品和环境化学品的化学毒性评估对人类健康有着至关重要的影响。评估化学毒性的传统动物模型昂贵、耗时，而且往往无法检测人类体内的有毒物质。计算毒理学是一种很有前途的替代方法，它利用机器学习 (ML) 和深度学习 (DL) 技术来预测化学品的潜在毒性。尽管基于机器学习和深度学习的计算模型在化学毒性预测中的应用很有吸引力，但许多毒性模型本质上是“黑匣子”，毒理学家难以解释，这阻碍了使用这些模型进行化学风险评估。计算机科学领域可解释机器学习（IML）的最新进展满足了揭示潜在毒性机制和阐明毒性模型领域知识的迫切需求。在这篇综述中，我们重点讨论了IML在计算毒理学中的应用，包括毒性特征数据、模型解释方法、知识库框架在IML开发中的使用以及最近的应用。还讨论了毒理学中 IML 建模的挑战和未来方向。我们希望这篇综述能够鼓励人们努力开发具有新 IML 算法的可解释模型，这些模型可以通过说明人类毒性机制来协助新的化学评估。

更新日期：2023-05-24

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