Leveraging Large Language Models for enzymatic reaction prediction and characterization

• URL: http://arxiv.org/abs/2505.05616v1
• Date: Thu, 08 May 2025 19:53:53 GMT
• Title: Leveraging Large Language Models for enzymatic reaction prediction and characterization
• Authors: Lorenzo Di Fruscia, Jana Marie Weber,
• Abstract summary: We systematically evaluate the capability of Large Language Models (LLMs) across three core biochemical tasks.<n>We compare single-task and multitask learning strategies, employing parameter-efficient fine-tuning via LoRA adapters.<n>Our results demonstrate that fine-tuned LLMs capture biochemical knowledge, with multitask learning enhancing forward- and retrosynthesis predictions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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