Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find structurally similar candidates is slow, error-prone, and requires deep domain expertise to interpret results. Building a protein research copilot can transform how researchers search for structurally similar peptides across large datasets — enabling natural language queries, automated embedding generation, and AI-powered result summarization in a single conversational interface.

This post shows you how to build a conversational protein research assistant that combines three capabilities:

Natural language query parsing to extract structured search parameters.

Vector similarity search over protein embeddings using a specialized language model.

AI-generated scientific summaries of search results.