基于低成本奖励的GFlowNet预训练
GFlowNet Pretraining with Inexpensive Rewards
AI总结：
针对现有GFlowNet用分子片段构建单元限制化学空间探索的问题，提出以原子为构建单元的A-GFN，通过基于低成本分子描述符的无监督预训练和目标条件微调，在ZINC15数据集上实现了优于基线的药物设计效果。
摘要：
生成流网络（Generative Flow Networks, GFlowNets）是一类生成模型，近期已成为一种通过学习未归一化奖励分布来生成多样且高质量分子结构的合适框架。该方向的以往研究通常使用预定义分子片段作为构建单元来限制探索，从而限制了可触及的化学空间。在本研究中，我们提出原子生成流网络（Atomic GFlowNets, A-GFNs）——一种以单个原子为构建单元的基础生成模型，可更全面地探索类药化学空间。我们提出一种利用离线类药分子数据集的无监督预训练方法，该方法使A-GFN以低成本但信息丰富的分子描述符为条件，例如类药性、拓扑极性表面积和合成可及性评分。这些性质作为代理奖励，引导A-GFN趋向于具有理想药理特性的化学空间区域。我们进一步实现了目标条件微调流程来改进方法，该流程可调整A-GFN以优化特定目标属性。本研究中，我们在ZINC15离线数据集上预训练A-GFN，并采用可靠的评估指标，证明了我们的方法在药物设计中相较于其他相关基线方法的有效性。
英文摘要：
Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.
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。
