用大模型预测酶反应，提升生物催化研发效率
Leveraging Large Language Models for enzymatic reaction prediction and characterization
- 用Llama-3.1大模型做酶反应预测，支持正向与逆向合成
- 多任务学习使反应预测准确率显著提升，尤其在数据少时表现好
- 适合做酶工程、药物发现的研究者参考
酶反应预测对生物催化、代谢工程和药物发现至关重要，但仍是复杂且耗资源的任务。大型语言模型（LLMs）在科学领域展现出强大潜力，具备知识泛化、复杂结构推理和上下文学习能力。本研究系统评估了Llama-3.1系列（8B和70B）在三大核心生化任务中的表现：酶委员会编号预测、正向合成与逆向合成。比较了单任务与多任务学习策略，采用参数高效微调（LoRA适配器）。还考察了不同数据规模下的表现，探索其在低数据场景的适应性。结果表明，微调后的LLM能有效捕捉生化知识，多任务学习通过共享酶学信息提升了正向与逆向合成预测性能。同时发现层级式EC分类存在挑战，指明了未来改进方向。
原文摘要 · Abstract (English)
Predicting enzymatic reactions is crucial for applications in biocatalysis, metabolic engineering, and drug discovery, yet it remains a complex and resource-intensive task. Large Language Models (LLMs) have recently demonstrated remarkable success in various scientific domains, e.g., through their ability to generalize knowledge, reason over complex structures, and leverage in-context learning strategies. In this study, we systematically evaluate the capability of LLMs, particularly the Llama-3.1 family (8B and 70B), across three core biochemical tasks: Enzyme Commission number prediction, forward synthesis, and retrosynthesis. We compare single-task and multitask learning strategies, employing parameter-efficient fine-tuning via LoRA adapters. Additionally, we assess performance across different data regimes to explore their adaptability in low-data settings. Our results demonstrate that fine-tuned LLMs capture biochemical knowledge, with multitask learning enhancing forward- and retrosynthesis predictions by leveraging shared enzymatic information. We also identify key limitations, for example challenges in hierarchical EC classification schemes, highlighting areas for further improvement in LLM-driven biochemical modeling.
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