.png){kind=icon}
.png){kind=icon}
Return
A Conversational Large-Language-Model Tutor that Accelerates Machine-Learning Method Development in Routine Bioanalytical Workflows
.png){kind=icon}
OA
AI
DOI：10.1002/cbic.202500678.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As machine learning (ML) becomes increasingly relevant in experimental chemistry, many scientists face barriers to adoption due to limited training in ML. While AutoML platforms offer powerful capabilities, they lack the instructional scaffolding needed by users without an ML background. To address this gap, a lightweight, conversational assistant is presented that guides users through ML workflow design using plain-language dialog. Powered by OpenAI's GPT-4o and deployed via a Gradio interface, the assistant operates under a structured system prompt that simulates pedagogical reasoning. It behaves like a domain-specific tutor: helping users define ML goals, assess data structure, select models, evaluate metrics, and generate annotated Python code. A complete documentation of the development process is provided, allowing researchers to adapt the system for other domains. Herein, its utility is demonstrated in two representative case studies: 1) image classification of lateral flow immunoassay test strips for diagnostic readout; and 2) regression-based prediction of liquid chromatography-mass spectrometry retention times from molecular descriptors for small molecules. In both cases, lab members with no ML experience successfully developed working models guided solely by the assistant. By lowering the barrier to ML adoption in data-rich analytical workflows, this system offers a customizable workflow for building domain-specific assistants across experimental science.
Keywords:
generative AI in biochemical science
machine learning education tool
machine learning in biochemical science
machine learning model design
prompt engineering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.