Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury,Chemical Research in Toxicology

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Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2020-12-21 , DOI: 10.1021/acs.chemrestox.0c00322
Hehuan Ma ₁ , Weizhi An ₁ , Yuhong Wang ₂ , Hongmao Sun ₂ , Ruili Huang ₂ , Junzhou Huang ₁

Affiliation

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.

中文翻译：

用于预测药物性肝损伤的属性增强的深度图学习

药物性肝损伤（DILI）是决定潜在药物是否合格的关键因素。然而，由于测试过程复杂，DILI性能极难获得。因此，一个计算机在药物发现的早期阶段进行筛选将有助于通过筛选那些具有高风险引起 DILI 的候选药物来降低总开发成本。为了达到筛选目标，我们应用了多种计算技术来预测 DILI 属性，包括传统的机器学习方法和基于图形的深度学习技术。虽然深度学习模型需要大量训练数据来调整巨大的模型参数，但 DILI 数据集仅包含几百个带注释的分子。为了缓解数据稀缺问题，我们提出了一种属性增强策略，将大量训练数据与其他属性信息结合起来。大量实验表明，我们提出的方法通过使用随机拆分的交叉验证获得 81.4% 的准确率，显着优于 DILI 数据集上的所有现有基线，

更新日期：2021-02-15

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