Leveraging Large Language Models for enzymatic reaction prediction and characterization
This addresses the resource-intensive problem of predicting enzymatic reactions for applications in biocatalysis, metabolic engineering, and drug discovery, representing an incremental advancement by applying existing LLM methods to biochemical data.
The study tackled enzymatic reaction prediction by evaluating fine-tuned LLMs like Llama-3.1 on tasks such as Enzyme Commission number prediction and synthesis, finding that multitask learning improved forward- and retrosynthesis predictions by leveraging shared enzymatic information.
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.