ChemFlow: Bridging Computational Tools and Bioinformatics Through LLM-Driven Workflow Automation

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Innovations in computational methodologies have significantly transformed the landscape of scientific research, in silico experiments have replaced some of the physical experiments. ChemFlow, a novel proof-of-concept platform introduced in this paper, coalesces these advancements by automating the creation of workflows. Designed specifically for the bioinformatics field, ChemFlow leverages Large Language Models and prompt engineering techniques to interpret natural language descriptions and convert them into executable workflows without the need for manual coding. Our contributions are two-fold: first, we introduce an innovative workflow generation and execution platform with the help of large language models, and second, we introduce a novel set of prompt optimisation strategies that improve both the accuracy and efficiency of the generated workflows. ChemFlow enables researchers to focus on domain-specific challenges rather than computational intricacies, making it a pivotal tool for advancing scientific productivity and innovation.

Original languageEnglish
Title of host publicationInternational Conference on Modelling and Development of Intelligent Systems
Pages123-137
Number of pages15
DOIs
Publication statusPublished - 2025
Event9th International Conference on Modelling and Development of Intelligent Systems, MDIS 2024 - Sibiu, Romania
Duration: 17 Oct 202419 Oct 2024

Publication series

NameCommunications in Computer and Information Science ((CCIS,volume 2486))

Conference

Conference9th International Conference on Modelling and Development of Intelligent Systems, MDIS 2024
Country/TerritoryRomania
CitySibiu
Period17/10/2419/10/24

Keywords

  • AI
  • Large Language Models
  • LLM
  • Prompt Optimisation
  • Workflow Generation

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