用智能分段与对话式优化，让药物研发生成更高效
FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization
- 将分子分段视为词汇选择问题，用强化学习动态优化分段与生成
- 人机协作模式下发现的高分分子数接近基线两倍
- 支持专家对话自动调优，适合医药研发人员快速试错
基于生成式AI的分子生成对药物发现至关重要，但类别特异数据集通常训练样本少于100个。尽管基于片段的方法比原子级方法在小样本下表现更好，现有启发式分段策略限制多样性并遗漏关键片段。此外，模型调优通常需要药物化学家与AI工程师缓慢协作。我们提出FRAGMENTA，一个端到端药物先导优化框架：1）一种新型生成模型，将分段重构为“词汇选择”问题，利用动态Q-learning联合优化分段与生成；2）一个代理型AI系统，通过领域专家的对话反馈迭代优化目标。该系统可脱离AI工程师直接运行，逐步学习领域知识实现自动化调优。在真实癌症药物发现实验中，人机协同配置发现的高分分子数量接近基线两倍；完全自主的代理-代理系统亦优于传统人-人调优，验证了代理调优在捕捉专家意图方面的有效性。
原文摘要 · Abstract (English)
Molecule generation using generative AI is vital for drug discovery, yet class-specific datasets often contain fewer than 100 training examples. While fragment-based models handle limited data better than atom-based approaches, existing heuristic fragmentation limits diversity and misses key fragments. Additionally, model tuning typically requires slow, indirect collaboration between medicinal chemists and AI engineers. We introduce FRAGMENTA, an end-to-end framework for drug lead optimization comprising: 1) a novel generative model that reframes fragmentation as a "vocabulary selection" problem, using dynamic Q-learning to jointly optimize fragmentation and generation; and 2) an agentic AI system that refines objectives via conversational feedback from domain experts. This system removes the AI engineer from the loop and progressively learns domain knowledge to eventually automate tuning. In real-world cancer drug discovery experiments, FRAGMENTA's Human-Agent configuration identified nearly twice as many high-scoring molecules as baselines. Furthermore, the fully autonomous Agent-Agent system outperformed traditional Human-Human tuning, demonstrating the efficacy of agentic tuning in capturing expert intent.
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。