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.

中文翻译：

---

神经网络激活相似性：协助化学毒理学决策的新措施

深度学习神经网络构建用于预测79种在药理上重要的人类生物学靶点上的化学结合，在测试数据上显示出极高的性能（准确度92.2±4.2％，MCC 0.814±0.093和ROC-AUC 0.96±0.04）。基于信号通过网络的传播，已经开发了一种新的分子相似性测量方法，即神经网络激活相似性。这是对标准Tanimoto相似性的补充，并且组合使用可以通过提供对基本依据的更好理解来提高对计算机预测新化学物质活性的信心。这些人类分子引发事件的计算机模拟对化学安全风险评估的未来至关重要，并提高了安全决策的效率。