Department of Computer Science, Hunter College, The City University of New York, New York, New York, USA.
Program in Computer Science, Biology & Biochemistry, The Graduate Center, The City University of New York, New York, New York, USA.
Expert Opin Drug Discov. 2022 Aug;17(8):849-863. doi: 10.1080/17460441.2022.2072288. Epub 2022 Aug 5.
Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complex diseases.
In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed.
In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized drug design.
许多多基因系统性疾病，如神经紊乱、炎症性疾病和大多数癌症，目前尚无有效的治疗方法。强化学习驱动的系统药理学是为无法治疗的复杂疾病设计个性化疗法的一种潜在有效方法。
本文综述了最新的强化学习方法及其在药物设计中的最新应用。讨论了在系统药理学和个性化医学中利用强化学习面临的挑战。提出了潜在的解决方案来克服这些挑战。
尽管先进的强化学习技术已成功应用于基于靶点的药物发现，但仍需要新的强化学习策略来解决面向系统药理学的个性化药物设计问题。
