Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK.
Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK; FabRx Ltd, 3 Romney Road, Ashford, TN24 0RW, UK.
Drug Discov Today. 2021 Mar;26(3):769-777. doi: 10.1016/j.drudis.2020.12.003. Epub 2020 Dec 5.
The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.
机器学习（ML）在药物发现中的应用日益普及，取得了令人瞩目的成果。随着其应用的增加，其局限性也日益明显。这些局限性包括对大数据的需求、数据稀疏性以及缺乏可解释性。此外，人们也逐渐认识到这些技术并非真正自主的，即使在部署后也需要重新训练。在这篇综述中，我们详细介绍了使用高级技术来规避这些挑战的方法，并从药物发现和相关学科中举例说明。此外，我们还介绍了新兴技术及其在药物发现中的潜在作用。本文介绍的技术预计将扩大机器学习在药物发现中的应用。
