论文标题
使用深厚的增强学习来更好地阿片类药物拮抗剂
Towards Better Opioid Antagonists Using Deep Reinforcement Learning
论文作者
论文摘要
阿片类药物拮抗剂纳洛酮已被广泛用于挽救阿片类药物过量的生命，阿片类药物过量是阿片类药物流行病的主要原因。但是，纳洛酮具有短脑保留能力，这限制了其治疗功效。开发更好的阿片类药物拮抗剂对于打击阿片类药物流行至关重要。目的是在巨大的化学空间中详尽搜索以寻求更好的阿片类药物拮抗剂，我们采用增强性学习，可以有效地基于梯度的搜索对具有所需物理化学和/或生物学特性的分子的有效搜索。具体而言，我们实施了深厚的增强学习框架，以发现潜在的铅化合物作为具有增强大脑保留能力的更好的阿片类拮抗剂。定制的多目标奖励函数旨在使生成偏向具有足够的阿片类拮抗作用和增强大脑保留能力的分子。彻底的评估表明，使用此框架，我们能够识别具有多种所需特性的有效，新颖和可行的分子，在药物发现方面具有很高的潜力。
Naloxone, an opioid antagonist, has been widely used to save lives from opioid overdose, a leading cause for death in the opioid epidemic. However, naloxone has short brain retention ability, which limits its therapeutic efficacy. Developing better opioid antagonists is critical in combating the opioid epidemic.Instead of exhaustively searching in a huge chemical space for better opioid antagonists, we adopt reinforcement learning which allows efficient gradient-based search towards molecules with desired physicochemical and/or biological properties. Specifically, we implement a deep reinforcement learning framework to discover potential lead compounds as better opioid antagonists with enhanced brain retention ability. A customized multi-objective reward function is designed to bias the generation towards molecules with both sufficient opioid antagonistic effect and enhanced brain retention ability. Thorough evaluation demonstrates that with this framework, we are able to identify valid, novel and feasible molecules with multiple desired properties, which has high potential in drug discovery.
