使用深度学习进行毒性预测
Toxicity Prediction using Deep Learning
摘要：
每天我们都通过食品添加剂、清洁和化妆品以及药物接触到各种化学物质，其中一些可能是有毒的。然而，通过生物实验测试所有现有化合物的毒性在经济上和后勤上都不可行。因此，美国国家卫生研究院、美国环保署和美国食品药品监督管理局在“21世纪毒理学”（Tox21）倡议中发起了Tox21数据挑战。这项挑战的目标是评估计算方法在预测化合物毒性方面的性能。最先进的毒性预测方法建立在几十年来开发的专门设计的化学描述符基础上。虽然深度学习是该领域的新技术，以前从未应用于毒性预测，但它显然优于所有其他参与方法。在这篇应用论文中，我们展示了深度网络自动学习类似于成熟的toxophores的特征。总的来说，我们的深度学习方法赢得了小组挑战（核受体和应激反应）以及整体大挑战，从而为毒性预测树立了新的标准。
英文摘要：
Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA and FDA launched the Tox21 Data Challenge within the "Toxicology in the 21st Century" (Tox21) initiative. The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds. State of the art toxicity prediction methods build upon specifically-designed chemical descriptors developed over decades. Though Deep Learning is new to the field and was never applied to toxicity prediction before, it clearly outperformed all other participating methods. In this application paper we show that deep nets automatically learn features resembling well-established toxicophores. In total, our Deep Learning approach won both of the panel-challenges (nuclear receptors and stress response) as well as the overall Grand Challenge, and thereby sets a new standard in tox prediction.
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