Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

doi:10.2174/0929867327666200907141016
摘要

药物-靶相互作用(DTIs)预测在药物发现中起着核心作用。DTIs预测中的计算方法由于进行大规模的体外和体内实验成本高且耗时长而受到越来越多的关注。机器学习方法，特别是深度学习，被广泛应用于DTIs预测。在本研究中，主要目的是提供一个全面的概述基于深度学习的DTIs预测方法。在这里，我们从多个角度研究现有的方法。我们探索这些方法，以找出哪些深度网络架构被用于提取药物化合物和蛋白质序列的特征。分析比较了各种体系结构的优缺点。此外，我们探讨了如何结合描述药物和蛋白质特征的过程。同样，研究了DTIs预测中常用的数据集列表。最后，讨论了当前面临的挑战，并对深度学习在DTI预测中的应用前景进行了展望。
关键词: 药物-靶标相互作用预测，深度学习，机器学习，药物发现，DTIs预测方法，EC50
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DOI https://dx.doi.org/10.2174/0929867327666200907141016	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
当代药物化学

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