Mol-AIR：面向目标导向分子生成的自适应内在奖励分子强化学习
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
AI总结：
Mol-AIR提出一种结合随机蒸馏网络与计数策略的自适应内在奖励强化学习框架，在无先验知识条件下提升目标导向分子生成性能，并在多项基准测试中优于现有方法。
摘要：
在基于人工智能的药物发现中，优化发现具有期望性质的分子结构的技术至关重要。将深度生成模型与强化学习相结合，已成为生成具有特定性质分子的有效策略。尽管具有潜力，该方法在探索广阔化学空间和优化特定化学性质方面仍效果不佳。为克服这些局限，我们提出了Mol-AIR，一个基于强化学习的框架，利用自适应内在奖励实现有效的目标导向分子生成。Mol-AIR通过利用随机蒸馏网络和基于计数的策略，结合了基于历史与基于学习的内在奖励的优势。在基准测试中，Mol-AIR在无任何先验知识的情况下生成具有期望性质的分子方面，表现出优于现有方法的性能，包括惩罚LogP、QED和塞来昔布相似度。我们相信，Mol-AIR代表了药物发现的重要进展，为发现新型治疗药物提供了更高效的路径。
英文摘要：
Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence(AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates superior performance over existing approaches in generating molecules with desired properties without any prior knowledge, including penalized LogP, QED, and celecoxib similarity. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
