Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.
Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.
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DOI：
10.1093/bib/bbz157
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发表时间：
2021-01-18
影响因子：
9.5
通讯作者：
Najarian K
中科院分区：
文献类型：
--
作者：
Bagherian M;Sabeti E;Wang K;Sartor MA;Nikolovska-Coleska Z;Najarian K
预测药物与靶点之间的相互作用在药物发现过程中起着关键作用。有必要开发新的和有效的预测方法，以避免昂贵的和费力的，但并不总是确定性的实验，以确定药物靶点相互作用（DTI）的实验。这些办法应能够及时查明潜在的贸易一体化机构。在这篇文章中，我们描述了DTI预测任务所需的数据，然后是一个由机器学习方法和数据库组成的综合目录，这些方法和数据库已被提出并用于预测DTI。并简要讨论了各方法的优缺点。最后，强调了使用机器学习方法预测DTI可能面临的挑战，并总结了未来重要的研究方向。
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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影响因子：
56.9
作者：
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通讯作者：
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DOI：
10.4018/jswis.2009081901
发表时间：
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