AIChatBio: An Artificial Intelligence Chatbot Model for Biological Knowledge Retrieval and Biomacromolecule Design

Conversational agents for bioinformatics data analysis and interpretation remain largely inaccessible to the broader biological research community. This gap is especially pronounced in the current Generative AI era, which demands a paradigm shift in how researchers interact with computational tools. There is a pressing need to bridge well-established biological infrastructures and databases with the capabilities of Generative AI to democratize access to bioinformatics insights. In this study, we present an integrated framework that connects the robust bioinformatics resources of the National Center for Biotechnology Information (NCBI) with Generative AI through a novel Artificial Intelligence Chatbot Model for Biological Knowledge Retrieval and Biomacromolecule Design, AIChatBio. This operational model positions Generative AI as an intelligent information hub. User interactions with the chatbot are enriched by real-time data retrieval from web portal of the biological databases hosted at NCBI, which are then translated into structured inquiries toward the web applications of NCBI and bioinformatics analysis tools. These inquiries are directed toward bioinformatics analysis tools to perform tasks such as sequence alignment and primer design. Additionally, the outputs generated by these tools are interpreted by the chatbot, allowing users to gain meaningful insights without requiring deep technical expertise in bioinformatics. To demonstrate the feasibility of this approach, we developed a prototype implementation that integrates PCR primer design using Primer-BLAST [1], literature interpretation via PubMed for general topics, and the LitVar2 for SNPs associated topics [23]. This system was built using TypeScript and the ChatGPT API combining the bioinformatics web applications from NCBI, and its source code is publicly available via GitHub and the Chrome extension is available at Chrome Web Store. Our work highlights the potential of Generative AI to transform biological data analysis workflows, making them more intuitive, accessible, and scalable for researchers across disciplines.