Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918468687757312 |
|---|---|
| author | Sinclair, Matthew Meigooni, Moeen Vasan, Archit Gokdemir, Ozan Lian, Xinran Ma, Heng Babuji, Yadu Brace, Alexander Hossain, Khalid Siebenschuh, Carlo Brettin, Thomas Chard, Kyle Henry, Christopher Vishwanath, Venkatram Stevens, Rick L. Foster, Ian T. Ramanathan, Arvind |
| author_facet | Sinclair, Matthew Meigooni, Moeen Vasan, Archit Gokdemir, Ozan Lian, Xinran Ma, Heng Babuji, Yadu Brace, Alexander Hossain, Khalid Siebenschuh, Carlo Brettin, Thomas Chard, Kyle Henry, Christopher Vishwanath, Venkatram Stevens, Rick L. Foster, Ian T. Ramanathan, Arvind |
| contents | Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain long disordered regions -- but their lack of stable secondary/tertiary structures makes them "undruggable". While recent computational advances, such as diffusion models, can design high-affinity IDP binders, translating these to practical drug discovery requires autonomous systems capable of reasoning across complex conformational ensembles and orchestrating diverse computational tools at scale.To address this challenge, we designed and implemented StructBioReasoner, a scalable multi-agent system for designing biologics that can be used to target IDPs. StructBioReasoner employs a novel tournament-based reasoning framework where specialized agents compete to generate and refine therapeutic hypotheses, naturally distributing computational load for efficient exploration of the vast design space. Agents integrate domain knowledge with access to literature synthesis, AI-structure prediction, molecular simulations, and stability analysis, coordinating their execution on HPC infrastructure via an extensible federated agentic middleware, Academy. We benchmark StructBioReasoner across Der f 21 and NMNAT-2 and demonstrate that over 50\% of 787 designed and validated candidates for Der f 21 outperformed the human-designed reference binders from literature, in terms of improved binding free energy. For the more challenging NMNAT-2 protein, we identified three binding modes from 97,066 binders, including the well-studied NMNAT2:p53 interface. Thus, StructBioReasoner lays the groundwork for agentic reasoning systems for IDP therapeutic discovery on Exascale platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins Sinclair, Matthew Meigooni, Moeen Vasan, Archit Gokdemir, Ozan Lian, Xinran Ma, Heng Babuji, Yadu Brace, Alexander Hossain, Khalid Siebenschuh, Carlo Brettin, Thomas Chard, Kyle Henry, Christopher Vishwanath, Venkatram Stevens, Rick L. Foster, Ian T. Ramanathan, Arvind Quantitative Methods Artificial Intelligence Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain long disordered regions -- but their lack of stable secondary/tertiary structures makes them "undruggable". While recent computational advances, such as diffusion models, can design high-affinity IDP binders, translating these to practical drug discovery requires autonomous systems capable of reasoning across complex conformational ensembles and orchestrating diverse computational tools at scale.To address this challenge, we designed and implemented StructBioReasoner, a scalable multi-agent system for designing biologics that can be used to target IDPs. StructBioReasoner employs a novel tournament-based reasoning framework where specialized agents compete to generate and refine therapeutic hypotheses, naturally distributing computational load for efficient exploration of the vast design space. Agents integrate domain knowledge with access to literature synthesis, AI-structure prediction, molecular simulations, and stability analysis, coordinating their execution on HPC infrastructure via an extensible federated agentic middleware, Academy. We benchmark StructBioReasoner across Der f 21 and NMNAT-2 and demonstrate that over 50\% of 787 designed and validated candidates for Der f 21 outperformed the human-designed reference binders from literature, in terms of improved binding free energy. For the more challenging NMNAT-2 protein, we identified three binding modes from 97,066 binders, including the well-studied NMNAT2:p53 interface. Thus, StructBioReasoner lays the groundwork for agentic reasoning systems for IDP therapeutic discovery on Exascale platforms. |
| title | Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2512.15930 |