用大模型生成可合成的分子及类似物，解决化学空间探索中的可合成性难题
SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
- 微调Llama3生成包含常见原料和稳定反应模板的完整合成路径
- 仅用少量数据即实现优于现有方法的正向与逆向合成规划性能
- 无需额外训练即可泛化到未见但可购得的原料，适合药物研发使用
用于探索化学空间的生成式机器学习模型潜力巨大，但许多生成的分子难以合成，限制了其进一步研究与开发。本文提出一种新方法，通过微调Meta的Llama3大语言模型构建SynLlama，能够生成由常见可得原料和稳健有机反应模板组成的完整合成路径。SynLlama在显著更少的数据下探索了广阔的可合成化学空间，在正向与逆向合成规划上均表现优异，超越当前先进方法。我们发现，即使不经过外部原料训练，SynLlama也能有效泛化至未见但可购得的原料，表明其重构能力可扩展至比训练数据更广的可合成化学空间。此外，我们在药物研发场景中展示了SynLlama在类似物合成规划及靶点抑制剂先导化合物拓展中的应用，为药物化学家提供有力工具。
原文摘要 · Abstract (English)
Generative machine learning models for exploring chemical space have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta's Llama3 Large Language Models (LLMs) to create SynLlama, which generates full synthetic pathways made of commonly accessible building blocks and robust organic reaction templates. SynLlama explores a large synthesizable space using significantly less data, and offers strong performance in both forward and bottom-up synthesis planning compared to other state-of-the-art methods. We find that SynLlama, even without training on external building blocks, can effectively generalize to unseen yet purchasable building blocks, meaning that its reconstruction capabilities extend to a broader synthesizable chemical space than the training data. We also demonstrate the use of SynLlama in a pharmaceutical context for synthesis planning of analog molecules and hit expansion leads for proposed inhibitors of target proteins, offering medicinal chemists a valuable tool for discovery.
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。