用成本引导生成分子，更便宜、更多样、更高效。
Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
- 用辅助模型估算合成成本，反向指导生成路径
- 在小分子库中仍保持高多样性与高质量，成本降低显著
- 适合药物研发中的快速分子设计，尤其关注成本控制
基于模板的分子生成通过预定义的反应模板和片段，确保生成化合物具有可合成性。本文针对模板式GFlowNets的三大挑战：（1）最小化合成成本，（2）扩展至大规模片段库，（3）有效利用小片段集，提出递归成本引导框架。该框架引入辅助机器学习模型近似合成成本与可行性，反向引导生成趋向低成本路径，显著提升成本效益、分子多样性和质量，尤其结合探索惩罚机制后平衡了探索与利用的权衡。为提升小片段库表现，我们设计动态片段库机制，重用高奖励中间状态构建完整合成树。该方法在模板式分子生成任务中达到当前最优性能。
原文摘要 · Abstract (English)
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose Recursive Cost Guidance, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an Exploitation Penalty that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a Dynamic Library mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。