Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library
Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library
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DOI：
10.3390/molecules25122764
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发表时间：
2020-06-01
期刊：
影响因子：
4.6
通讯作者：
Uesawa, Yoshihiro
中科院分区：
文献类型：
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作者：
Matsuzaka, Yasunari;Uesawa, Yoshihiro
核受体(NRs)与化学物质的相互作用可引起内分泌信号通路的失调，从而导致自然荷尔蒙的干扰而导致不良的健康后果。因此，识别NRS的可能配体是理解人类毒性的不良结局途径(AOP)以及开发新药的关键任务。然而，对新型配体的实验评估仍然昂贵且耗时。因此，一种具有广泛应用范围的计算机方法取代实验检验是非常可取的。最近发展起来的基于分子图像的深度学习方法DeepSnap-DL可以从三维化学结构中产生多个快照，并在毒理学评估中预测化学物质方面取得了很高的性能。在本研究中，我们使用DeepSnap-DL构建了35个NRS的激动剂和拮抗剂变构调节剂对来自Tox21 10K文库的化学物质的预测模型。我们展示了DeepSnap-DL在构建预测模型方面的高性能。这些发现可能有助于解释毒性的关键分子事件，并支持机器学习新领域的发展，以识别具有与NR信号通路相互作用的潜在环境化学物质。
The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.