Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges

• URL: http://arxiv.org/abs/2506.20598v1
• Date: Wed, 25 Jun 2025 16:37:46 GMT
• Title: Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges
• Authors: Alexander D. Kalian, Jaewook Lee, Stefan P. Johannesson, Lennart Otte, Christer Hogstrand, Miao Guo,
• Abstract summary: We present a proof-of-concept multi-agent Artificial Intelligence framework to support sustainable protein production research.<n>A literature search agent retrieves relevant scientific literature on microbial protein production for a specified microbial strain.<n>An information extraction agent processes the retrieved content to extract relevant biological and chemical information.
• Score: 38.405196084093454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The global demand for sustainable protein sources has accelerated the need for intelligent tools that can rapidly process and synthesise domain-specific scientific knowledge. In this study, we present a proof-of-concept multi-agent Artificial Intelligence (AI) framework designed to support sustainable protein production research, with an initial focus on microbial protein sources. Our Retrieval-Augmented Generation (RAG)-oriented system consists of two GPT-based LLM agents: (1) a literature search agent that retrieves relevant scientific literature on microbial protein production for a specified microbial strain, and (2) an information extraction agent that processes the retrieved content to extract relevant biological and chemical information. Two parallel methodologies, fine-tuning and prompt engineering, were explored for agent optimisation. Both methods demonstrated effectiveness at improving the performance of the information extraction agent in terms of transformer-based cosine similarity scores between obtained and ideal outputs. Mean cosine similarity scores were increased by up to 25%, while universally reaching mean scores of $\geq 0.89$ against ideal output text. Fine-tuning overall improved the mean scores to a greater extent (consistently of $\geq 0.94$) compared to prompt engineering, although lower statistical uncertainties were observed with the latter approach. A user interface was developed and published for enabling the use of the multi-agent AI system, alongside preliminary exploration of additional chemical safety-based search capabilities

Related papers

• A Multi-Agent System Enables Versatile Information Extraction from the Chemical Literature [8.306442315850878]
  We develop a multimodal large language model (MLLM)-based multi-agent system for robust and automated chemical information extraction.<n>Our system achieved an F1 score of 80.8% on a benchmark dataset of sophisticated multimodal chemical reaction graphics from the literature.
  arXiv  Detail & Related papers  (2025-07-27T11:16:57Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [80.08310253195144]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM [7.808231572590279]
  We propose a novel approach to achieve the same results from unannotated full documents using general large language models (LLMs) with lower hardware and labor costs.<n>Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE)<n>To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles.
  arXiv  Detail & Related papers  (2025-05-02T07:33:20Z)
• LLM Agent Swarm for Hypothesis-Driven Drug Discovery [2.7036595757881323]
  PharmaSwarm is a unified multi-agent framework that orchestrates specialized "agents" to propose, validate, and refine hypotheses for novel drug targets and lead compounds.<n>By acting as an AI copilot, PharmaSwarm can accelerate translational research and deliver high-confidence hypotheses more efficiently than traditional pipelines.
  arXiv  Detail & Related papers  (2025-04-24T22:27:50Z)
• Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization [51.104444856052204]
  We present MultiMol, a collaborative large language model (LLM) system designed to guide multi-objective molecular optimization.<n>In evaluations across six multi-objective optimization tasks, MultiMol significantly outperforms existing methods, achieving a 82.30% success rate.
  arXiv  Detail & Related papers  (2025-03-05T13:47:55Z)
• From Intention To Implementation: Automating Biomedical Research via LLMs [30.32209981487504]
  This paper introduces BioResearcher, the first end-to-end automated system designed to streamline the entire biomedical research process involving dry lab experiments.<n>BioResearcher employs a modular multi-agent architecture, integrating specialized agents for search, literature processing, experimental design, and programming.<n>The generated protocols, on average, outperform typical agent systems by 22.0% on five quality metrics.
  arXiv  Detail & Related papers  (2024-12-12T16:35:05Z)
• Automating Exploratory Proteomics Research via Language Models [22.302672656499315]
  PROTEUS is a fully automated system for scientific discovery from raw data. It produces a comprehensive set of research objectives, analysis results and novel biological hypotheses without human intervention.
  arXiv  Detail & Related papers  (2024-11-06T08:16:56Z)
• BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction [65.93303145891628]
  BatGPT-Chem is a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Our model captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions. This development empowers chemists to adeptly address novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science.
  arXiv  Detail & Related papers  (2024-08-19T05:17:40Z)
• An Autonomous Large Language Model Agent for Chemical Literature Data Mining [60.85177362167166]
  We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.