TY - GEN
T1 - ChemFlow
T2 - 9th International Conference on Modelling and Development of Intelligent Systems, MDIS 2024
AU - Marcu, Stefan Bogdan
AU - Mi, Yanlin
AU - Tangney, Mark
AU - Tabirca, Sabin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - AI
KW - Large Language Models
KW - LLM
KW - Prompt Optimisation
KW - Workflow Generation
UR - https://www.scopus.com/pages/publications/105005933369
U2 - 10.1007/978-3-031-87386-7_9
DO - 10.1007/978-3-031-87386-7_9
M3 - Conference proceeding
AN - SCOPUS:105005933369
T3 - Communications in Computer and Information Science ((CCIS,volume 2486))
SP - 123
EP - 137
BT - International Conference on Modelling and Development of Intelligent Systems
Y2 - 17 October 2024 through 19 October 2024
ER -