Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey
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DOI：
10.1093/bib/bby002
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发表时间：
2019-07-01
影响因子：
9.5
通讯作者：
Kwoh, Chee-Keong
中科院分区：
文献类型：
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作者：
Ezzat, Ali;Wu, Min;Kwoh, Chee-Keong
药物-靶点相互作用（DTI）的计算预测已成为药物发现过程中的一项重要任务。它通过众所周知昂贵且耗时的湿实验室实验建议潜在的相互作用候选者进行验证，从而缩小了相互作用的搜索空间。在本文中，我们旨在对计算 DTI 预测技术进行全面的概述和实证评估，为我们的研究人员提供指导和参考。具体来说，我们首先描述此类计算 DTI 预测工作中使用的数据。然后，我们对预测 DTI 的最先进方法进行分类和阐述。接下来，进行实证比较，展示一些代表性方法在不同场景下的预测性能。我们还提出了评估研究中有趣的发现，讨论了每种方法的优点和缺点。最后，我们强调了进一步增强 DTI 预测性能的潜在途径以及相关研究方向。
Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.