DeepTox: Toxicity Prediction using Deep Learning
DeepTox: Toxicity Prediction using Deep Learning
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DOI：
10.3389/fenvs.2015.00080
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发表时间：
2016-01-01
影响因子：
4.6
通讯作者：
Hochreiter, Sepp
中科院分区：
文献类型：
--
作者：
Mayr, Andreas;Klambauer, Gunter;Hochreiter, Sepp
Tox 21数据挑战是科学界为比较毒性预测的计算方法所做的最大努力。这项挑战包括12，000种环境化学品和药物，通过专门设计的测定方法测量了12种不同的毒性作用。我们参与了这项挑战，以评估深度学习在计算毒性预测中的性能。深度学习已经彻底改变了图像处理，语音识别和语言理解，但尚未应用于计算毒性。深度学习建立在人工神经网络的新算法和架构以及最近可用的非常快速的计算机和大规模数据集的基础上。它发现输入的多个层次的分布式表示，更高的层次表示更抽象的概念。我们假设，化学特征层次结构的构建使深度学习比其他毒性预测方法更具优势。此外，深度学习自然能够实现多任务学习，即在一个神经网络中学习所有毒性效应，从而学习高度信息化的化学特征。为了利用深度学习进行毒性预测，我们开发了DeepTox管道。首先，DeepTox对化合物的化学表示进行标准化。然后，它计算大量的化学描述符，用作机器学习方法的输入。在下一步中，DeepTox训练模型，评估它们，并将其中最好的组合到集合中。最后，DeepTox预测新化合物的毒性。在Tox 21 Data Challenge中，DeepTox在所有计算方法中表现最好，赢得了大挑战、核受体小组、应激反应小组和六个单一测定（Bioinf@JKU团队）。我们发现深度学习在毒性预测方面表现出色，并且优于许多其他计算方法，如朴素贝叶斯，支持向量机和随机森林。
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams Bioinf@JKU"). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.