AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design

• URL: http://arxiv.org/abs/2602.00019v1
• Date: Fri, 16 Jan 2026 08:57:03 GMT
• Title: AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design
• Authors: Fukang Ge, Jiarui Zhu, Linjie Zhang, Haowen Xiao, Xiangcheng Bao, Fangnan Xie, Danyang Chen, Yanrui Lu, Yuting Wang, Ziqian Guan, Lin Gu, Jinhao Bi, Yingying Zhu,
• Abstract summary: We present an agentic end-to-end drug design framework that leverages a Large Language Model (LLM) and the Model Context Protocol (MCP)<n>The system integrates four state-of-the-art components: MaSIF for geometric deep learning-based identification of protein-site interaction sites, Rosetta for grafting protein fragments onto protein backbones, ProteinMPNN for amino acid sequences, and AlphaFold3 for near-protein accuracy in complex structure prediction.
• Score: 8.190052071911001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern AI technologies for drug discovery are distributed across heterogeneous platforms-including web applications, desktop environments, and code libraries-leading to fragmented workflows, inconsistent interfaces, and high integration overhead. We present an agentic end-to-end drug design framework that leverages a Large Language Model (LLM) in conjunction with the Model Context Protocol (MCP) to dynamically coordinate access to biochemical databases, modular toolchains, and task-specific AI models. The system integrates four state-of-the-art components: MaSIF (MaSIF-site and MaSIF-seed-search) for geometric deep learning-based identification of protein-protein interaction (PPI) sites, Rosetta for grafting protein fragments onto protein backbones to form mini proteins, ProteinMPNN for amino acid sequences redesign, and AlphaFold3 for near-experimental accuracy in complex structure prediction. Starting from a target structure, the framework supports de novo binder generation via surface analysis, scaffold grafting and pose construction, sequence optimization, and structure prediction. Additionally, by replacing rigid, script-based workflows with a protocol-driven, LLM-coordinated architecture, the framework improves reproducibility, reduces manual overhead, and ensures extensibility, portability, and auditability across the entire drug design process.

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