Pepti-Agent: An AI Agent for Peptide Design and Optimization
Researchers have developed Pepti-Agent, a novel AI framework designed for the design and optimization of therapeutic peptides. This system integrates generative models with property predictors, allowing for iterative refinement of peptide sequences to balance competing constraints like solubility and hemolytic activity. Unlike previous monolithic scripts, Pepti-Agent utilizes a Model Context Protocol (MCP) to expose generation, prediction, and mutation tools independently, enabling a large language model controller to guide refinement based on live property profiles rather than solely on natural language reasoning. The framework also provides a reproducible trace of decisions and mutations for benchmarking and candidate prioritization. AI
IMPACT This framework could accelerate drug discovery by improving the efficiency and reproducibility of peptide design.