Wuxi School of Medicine, Jiangnan University, Wuxi, China.
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
Sci Rep. 2020 Apr 22;10(1):6870. doi: 10.1038/s41598-020-63842-7.
A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.
一种用于预测 DTIs 的计算技术现已成为药物发现过程中不可或缺的工作。它通过提出可能的相互作用竞争者来缩小相互作用的探索空间，通过湿实验室实验进行验证，湿实验室实验以昂贵和耗时而闻名。化学生物组学是一个新兴的研究领域，专注于系统地研究广泛系列的小分子配体对广泛的大分子靶标点的生物学影响。此外，随着时间的推移，算法的复杂性不断增加，这可能导致大数据技术（如 Spark）很快进入该领域。在本研究中，我们旨在提供对计算药物靶标相互作用预测方法的全面性和现实性评估，为从事类似方向研究的研究人员提供指导和参考。具体来说，我们首先解释用于计算药物靶标相互作用预测尝试的数据。然后，我们对用于预测 DTIs 的最佳和最现代技术进行分类和解释。然后，进行现实评估以显示几种说明性方法在各种情况下的预测性能。最终，我们强调了药物靶标相互作用预测实施和相关研究目标的进一步改进的可能机会。
