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Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.
Human Heredity ( IF 1.8 ) Pub Date : 2018-10-23 , DOI: 10.1159/000492574
Jeanette Prinz 1 , Mohamad Koohi-Moghadam 1, 2 , Hongzhe Sun 2 , Jean-Pierre A Kocher 1 , Junwen Wang 3, 4
Affiliation  

AIMS We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. METHODS We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. RESULTS We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. CONCLUSION Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

中文翻译:

基于药物副作用和全基因组关联研究的新型神经网络方法预测药物-靶标相互作用。

目的我们提出了一种新颖的机器学习方法来扩展有关药物-靶标相互作用的知识。我们的方法可能有助于制定有效的,危害较小的治疗策略,并能够检测现有药物的新适应症。方法我们开发了一种新颖的机器学习策略,可根据全基因组关联研究中的药物副作用和性状预测药物-靶标相互作用。我们整合了来自SIDER和GWASdb数据库的数据,并通过神经网络方法以独特的方式利用了它们。结果我们使用来自STITCH数据库的药物-靶标相互作用验证了我们的方法。此外,我们比较了预期目标与正在考虑中的药物的已知目标的化学相似性,并为预测的相互作用提供了基于文献的证据。我们发现针对我们预测针对同一蛋白质的药物的药物组合警告,提示协同效应加剧了有害事件。这证实了我们方法的转化价值,因为我们能够检测到由于共同的作用机制而应谨慎使用的药物。结论综上所述,我们得出的结论是,我们的方法能够对药物作用的分子决定因素产生新颖且可临床应用的见解。
更新日期:2019-11-01
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