Predicting adverse drug reactions through interpretable deep learning framework
Predicting adverse drug reactions through interpretable deep learning framework
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DOI：
10.1186/s12859-018-2544-0
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发表时间：
2018-12-28
影响因子：
3
通讯作者：
Zhang, Ping
中科院分区：
文献类型：
--
作者：
Dey, Sanjoy;Luo, Heng;Zhang, Ping
研究背景：药物不良反应（ADR）是指正常使用药物引起的非预期的有害反应。在药物开发的早期阶段预测和预防ADR有助于提高药物安全性并降低财务成本。方法：在本文中，我们开发了包括深度学习框架的机器学习模型，该模型可以同时预测ADR并识别与这些ADR相关的分子子结构，而无需先验定义子结构。我们使用十种不同的最先进的指纹模型评估了我们模型的性能，发现来自深度学习模型的神经指纹在预测ADR方面优于所有其他方法。通过对药物结构的特征分析，我们确定了与特定ADR相关的重要分子子结构，并通过统计分析评估了它们的关联性。结论：具有特征分析、子结构识别和统计评估的深度学习模型为识别分子结构中的风险成分提供了一种有前途的解决方案，并可能有助于改善药物安全性评估。
Background: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.Methods: In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.Results: We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.Conclusions: The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.