Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?
Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?
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DOI：
10.1021/acs.chemrestox.3c00171
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发表时间：
2023-09
影响因子：
4.1
通讯作者：
J. Kostal
中科院分区：
文献类型：
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作者：
J. Kostal
使用量子力学（QM）长期以来一直是化学和生物化学中研究共价结合现象的规范。制药行业利用QM模型明确地在共价药物发现和隐含地表征短程相互作用的非共价结合。预测毒理学一直抵制QM的广泛采用，包括在制药行业，尽管它与不良结果途径上游的代谢过程具有明显的相关性，并且QM方法和计算资源都有进步，这支持在合理的时间范围内适合目的的应用。在这里，我们使拥抱质量管理作为一个毒理学家的工具包不可或缺的一部分的情况下。我们认为，QM提供了必要的正交性，以警报为基础的专家系统和传统的定量构效关系，符合要求的商业化学品的安全性评估的无动物综合测试策略。我们概述了现有的障碍，这种过渡，包括需要培训模型开发人员在QM和转向基于服务的毒性模型，利用高性能计算集群。最后，我们描述了最近的例子，成功实施QM的危害评估，并提出如何在硅片毒理学可以进一步推进集成QM与人工智能。
The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.