强化学习用于复杂疾病的个性化药物发现和设计：系统药理学视角
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective
摘要：
许多多基因系统性疾病，如神经系统疾病，炎症性疾病和大多数癌症尚未得到有效的治疗。强化学习动力系统药理学是为无法治疗的复杂疾病设计个性化疗法的潜在有效方法。本文综述了强化学习方法及其在药物设计中的最新应用。利用强化学习的系统药理学和个性化医疗的挑战进行了讨论。提出了克服这些挑战的潜在解决方案。尽管先进的强化学习技术已成功应用于靶向药物发现，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从头药物设计。
英文摘要：
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 design 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 de novo drug design.
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