An AI-Driven Peptide Optimization Chatbot for Therapeutic Design: Integrating Machine Learning Models with Pharmaceutical Formulation Intelligence

Artificial intelligence (AI) is transforming therapeutic discovery by enabling computational prioritization of candidate molecules before costly experimental evaluation. In this study, we present an AI-assisted Peptide Optimization Chatbot that integrates machine learning (ML), conversational artificial intelligence, and pharmaceutical formulation intelligence into a unified computational framework for early-stage therapeutic peptide design. The platform supports peptide optimization for glucagon-like peptide-1 (GLP-1)-based metabolic disorders and provides a conceptual framework for future expansion to additional therapeutic indications, including immunomodulatory peptide discovery. The proposed architecture combines a Large Language Model (LLM)-based conversational interface with disease-specific predictive machine learning models deployed through a modular FastAPI backend and Streamlit web application. The LLM functions as an orchestration and explanation layer, translating user requests into computational workflows, while Random Forest models perform quantitative prediction and candidate prioritization. Candidate peptide sequences are generated through systematic mutation strategies, transformed into physicochemical descriptors, evaluated using supervised learning models, and ranked according to predicted therapeutic relevance. Unlike conventional peptide discovery workflows that rely primarily on experimental screening, the proposed platform integrates formulation-aware design principles, feature engineering, explainability, and interactive decision support within a single computational environment. Quantitative evaluation of the current GLP-1 regression model demonstrates that predictive performance remains limited because of the relatively small and heterogeneous dataset. Consequently, the present implementation should be regarded as a hypothesis-generation and decision-support framework rather than a clinically validated predictive system. Preliminary biological studies of GLP-1 prodrug biodistribution further demonstrate the potential translational utility of integrating computational prioritization with experimental validation while emphasizing that additional biological studies remain necessary. The proposed framework illustrates how conversational AI, interpretable machine learning, and pharmaceutical formulation intelligence can be combined to accelerate early-stage therapeutic peptide engineering while providing an extensible foundation for future incorporation of advanced deep learning models and larger experimental datasets.