School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
College of Humanities and Sciences of Northeast Normal University, Changchun 130117, China.
Curr Top Med Chem. 2020;20(21):1858-1867. doi: 10.2174/1568026620666200710101307.
Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.
抗癌药物筛选可以加速药物发现，挽救癌症患者的生命，但癌症异质性使得这种筛选具有挑战性。抗癌药物敏感性预测对于抗癌药物开发和药物敏感性生物标志物的识别很有用。深度学习作为机器学习的一个分支，是计算机研究的一个重要方面。其出色的计算性能意味着它已被用于许多生物医学用途，如医学图像解释、生物序列分析和药物发现。已有几项研究基于深度学习算法预测了抗癌药物的敏感性。在模型性能和多组学数据集成方面，深度学习领域已经取得了进展。然而，深度学习受到研究数量和可用数据源的限制，因此作为癌症药物筛选过程中的临床前方法并不完美。提高深度学习模型的性能是研究人员面临的紧迫问题。在这篇综述中，我们介绍了抗癌药物敏感性预测的研究以及深度学习在这一研究领域的应用。为未来的研究提供参考，我们还回顾了一些常见的数据源和机器学习方法。最后，我们讨论了深度学习的优缺点，以及该方法的局限性和未来展望。
