Neural network activation similarity: a new measure to assist decision making in chemical toxicology.
Neural network activation similarity: a new measure to assist decision making in chemical toxicology.
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DOI：
10.1039/d0sc01637c
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发表时间：
2020-06-24
期刊：
影响因子：
8.4
通讯作者：
Russell PJ
中科院分区：
文献类型：
--
作者：
Allen TEH;Wedlake AJ;Gelžinytė E;Gong C;Goodman JM;Gutsell S;Russell PJ
深度学习神经网络用于79个重要的人体生物学靶点的化学结合预测，在测试数据上表现出极高的性能（准确率92.2±4.2%,MCC 0.814±0.093,ROC-AUC 0.96±0.04）。基于信号在网络中的传播，提出了一种新的分子相似度度量方法——神经网络激活相似度。这是对标准谷本相似性的补充，并且通过提供对潜在理由的更好理解，组合使用增加了对计算机预测新化学品活性的信心。这些人类分子启动事件的计算机预测是未来化学品安全风险评估的核心，可以提高安全决策的效率。深度学习神经网络用于79个重要的人体生物学靶点的化学结合预测，在测试数据上表现出极高的性能（准确率92.2±4.2%,MCC 0.814±0.093,ROC-AUC 0.96±0.04）。
Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making. Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).
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影响因子：
--
作者：
Kandárová H;Letašiová S
通讯作者：
Letašiová S
用二元和三元分类方法计算机模拟化学致癌性估计
影响因子：
3.6
作者：
Li, Xiao;Du, Zheng;Tang, Yun
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Tang, Yun
影响因子：
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影响因子：
7.3
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通讯作者：
Tropsha, Alexander
影响因子：
3.9
作者：
Li, Xiao;Chen, Yaojie;Song, Xinrui;Zhang, Yuan;Li, Huanhuan;Zhao, Yong
通讯作者：
Zhao, Yong