Knowledge-guided deep learning models of drug toxicity improve interpretation.
Knowledge-guided deep learning models of drug toxicity improve interpretation.
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DOI：
10.1016/j.patter.2022.100565
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发表时间：
2022-09-09
期刊：
影响因子：
6.5
通讯作者：
Moore, Jason H.
中科院分区：
文献类型：
--
作者：
Hao, Yun;Romano, Joseph D.;Moore, Jason H.
在药物开发中，流失的一个主要原因是缺乏对控制药物毒性的细胞机制的理解。传统分类模型的黑箱性质限制了它们在确定毒性途径方面的效用。在这里，我们开发了DTox（毒理学深度学习），这是一种知识引导神经网络的解释框架，可以预测化合物对毒性测定的反应，并推断单个化合物的毒性途径。我们证明了DTox可以实现与传统模型相同的预测性能水平，并且在可解释性方面有显着改善。使用DTox，我们能够重新发现三种核受体的转录激活机制，概括芳香酶抑制剂和PXR受体（PXR）激动剂诱导的细胞活性，并区分导致HepG2细胞毒性的独特机制。DTox虚拟筛选显示，具有预测细胞毒性的化合物对临床肝脏表型的风险较高。总之，DTox提供了一个框架，破译细胞机制的毒性在硅片。DTox是一种深度学习的毒性预测模型，具有广泛的适用性它提供了一个解释框架来推断化合物的毒性途径我们在不牺牲准确性的情况下提高了毒性预测的可解释性DTox的解释框架在计算机上破译了毒性的细胞机制在药物开发中，流失的一个主要原因是缺乏对控制药物毒性的细胞机制的理解。用有限的先验知识解释新开发的化合物的毒性结果是具有挑战性的。为了应对这一挑战，我们提出了DTox（毒理学深度学习），这是一种结合了途径本体的广泛知识的深度学习模型。DTox是一种高效的学习模型，具有良好的预测性能。它适用于所有化合物，因为它只需要化学结构作为模型输入。更重要的是，DTox的知识引导结构使我们能够通过靶蛋白、功能途径和一般生物过程来识别将查询化合物与毒性结果连接起来的网络路径。这些途径可被视为毒性的机制解释，并有助于实验研究。我们利用现有的实验数据集来验证DTox的机制解释，并证明其生物学意义。毒性评价是药物开发的关键步骤。为了克服传统分类模型的黑箱性质，郝等人。提出了一个名为DTox（毒理学深度学习）的可解释模型，用于预测化合物对毒性测定的反应，并推断单个化合物的毒性途径。使用实验数据集的验证研究证明了DTox在重新发现已知机制，区分独特机制和重现导致毒性的细胞活动方面的有效性。DTox通过产生可检验的假说以供进一步研究，将有利于毒理学的机制研究。
In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (deep learning for toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and pregnane X receptor (PXR) agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico. DTox is a deep learning model for toxicity prediction with broad applicability It provides an interpretation framework to infer toxicity pathways of compounds We improve the interpretability of toxicity prediction without sacrificing accuracy DTox’s interpretation framework deciphers cellular mechanisms of toxicity in silico In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. It is challenging to explain the toxicity outcomes of newly developed compounds with limited prior knowledge. To address the challenge, we present DTox (Deep learning for Toxicology), a deep learning model incorporated with extensive knowledge from pathway ontology. DTox is a highly efficient learning model with good predictive performance. It is applicable to all compounds because it requires only chemical structure as model input. More importantly, the knowledge-guided structure of DTox enables us to identify network paths connecting query compounds to toxicity outcomes via target proteins, functional pathways, and general biological processes. Such paths can be viewed as mechanistic interpretation of toxicity and facilitate experimental investigation. We employ existing experimental datasets to validate the mechanistic interpretation by DTox and demonstrate its biological significance. Toxicity assessment is a critical step in drug development. To overcome the black-box nature of conventional classification models, Hao et al. propose an interpretable model named DTox (deep learning for toxicology) for predicting compound response to toxicity assays and inferring toxicity pathways of individual compounds. Validation studies using experimental datasets demonstrate the effectiveness of DTox in rediscovering known mechanisms, differentiating distinctive mechanisms, and recapitulating cellular activities leading to toxicity. DTox will benefit mechanistic studies in toxicology by generating testable hypotheses for further investigation.
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