AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917235990200320 |
|---|---|
| author | Ge, Fukang Zhu, Jiarui Zhang, Linjie Xiao, Haowen Bao, Xiangcheng Xie, Fangnan Chen, Danyang Lu, Yanrui Wang, Yuting Guan, Ziqian Gu, Lin Bi, Jinhao Zhu, Yingying |
| author_facet | Ge, Fukang Zhu, Jiarui Zhang, Linjie Xiao, Haowen Bao, Xiangcheng Xie, Fangnan Chen, Danyang Lu, Yanrui Wang, Yuting Guan, Ziqian Gu, Lin Bi, Jinhao Zhu, Yingying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00019 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design Ge, Fukang Zhu, Jiarui Zhang, Linjie Xiao, Haowen Bao, Xiangcheng Xie, Fangnan Chen, Danyang Lu, Yanrui Wang, Yuting Guan, Ziqian Gu, Lin Bi, Jinhao Zhu, Yingying Biomolecules Artificial Intelligence 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. |
| title | AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2602.00019 |