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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >End-to-End Representation Learning for Chemical-Chemical Interaction Prediction
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End-to-End Representation Learning for Chemical-Chemical Interaction Prediction

机译：用于化学-化学相互作用预测的端到端表示学习

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摘要

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. In addition, DeepCCI was experimentally validated to guarantee the commutative property. The automatically extracted features can alleviate the efforts required for manual feature engineering and improve prediction performance.
机译：化学-化学相互作用（CCI）在预测候选药物，毒性，治疗效果和生物学功能方面起主要作用。通常从各种信息中推断出CCI。但是，尚未使用基于学习的方法来预测CCI。在其他药物分析中，近年来，深度学习已得到积极应用。但是，在大多数情况下，即使深度学习具有特征提取功能，它也仅用于分类。因此，在本文中，我们提出了一种用于CCI的端到端表示学习方法，称为DeepCCI，该方法包括特征提取和基于学习的方法。我们提出的体系结构基于暹罗网络。隐藏的表示形式是从简化的分子输入线输入系统（SMILES）中提取的，该系统是使用权重共享卷积神经网络表示化学结构的字符串表示法。随后，测量两个提取的隐藏表示之间的L1元素方向距离。将DeepCCI的性能与12种指纹方法组合的性能进行了比较。提议的DeepCCI在大多数使用的评估指标中显示出最佳性能。此外，DeepCCI经过实验验证，可确保交换特性。自动提取的特征可以减轻手动特征工程所需的工作量，并提高预测性能。

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