论文标题
针对复杂疾病的个性化药物发现和设计的强化学习：系统药理学观点
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective
论文作者
论文摘要
许多多类别的全身性疾病，例如神经系统疾病，炎症性疾病以及大多数癌症尚无有效的治疗方法。强化学习动力系统药理学是一种设计针对不可治疗的复杂疾病的个性化疗法的潜在有效方法。在这项调查中，审查了最新的强化学习方法及其对药物设计的最新应用。讨论了针对系统药理学和个性化医学的增强学习的挑战。提出了克服挑战的潜在解决方案。尽管成功地将高级强化学习技术应用于基于目标的药物发现中，但仍需要新的增强学习策略来解决以药理学为导向的个性化的从头开始药物设计。
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.
