快速筛选可能导致特定毒血症的化学品

Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist
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引用次数: 0

摘要

毒性反应是由特定毒性作用引起的症状和体征模式,可为紧急治疗提供指导。通过计算识别可引起不同毒性反应的化学物质,我们可以快速筛选出新型化合物和化合物类别的潜在毒性。当前研究的目的是创建一个计算工具集,将化学物质与其潜在毒性反应进行映射。因此,我们评估了最先进的深度学习方法--最近开发的通信信息传递神经网络(CMPNN)--的性能,看其是否能克服使用小数据集训练深度学习模型的问题。我们的研究结果表明,多任务训练--一种以能够使用多个小型数据集来训练传统深度神经网络而著称的技术--与 CMPNN 的效果相当。我们还发现,基于 CMPNN 的集合学习比使用单一 CMPNN 模型获得的预测结果更可靠。此外,我们还证明,CMPNN 模型集合中单个模型预测的标准偏差与集合预测的误差相关,可用于估计集合预测的可靠性。对于没有明确分子机制或足够数据来训练深度学习模型的毒物,我们使用相似性集合方法来开发基于分子结构相似性的毒物模型。我们通过 https://toxidrome.bhsai.org/ 网站上的网络用户界面公开了本研究中开发的工具集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid screening of chemicals for their potential to cause specific toxidromes
Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.
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