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Research paper Hugging Face

Chamaileon: A Unified Framework for Multi-Target and Multi-State Protein Binder Design

AI By Crimson AI Hugging Face Papers 28 July 2026 · 00:00 11 views
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Researchers introduce Chamaileon, a framework that unifies multi-target and multi-state protein binder design using in-context co-design and mixed sampling, enabling a single sequence to adapt to diverse conformational landscapes.

Chamaileon: A Unified Framework for Multi-Target and Multi-State Protein Binder Design

Key points

Protein binder design is a crucial task in structural biology, but existing methods typically assume a single target in a single structural state. This limits their applicability to real-world scenarios where a binder must function across multiple conformations or targets. To address this, researchers have developed Chamaileon, a unified framework for cross-context binder design.

Chamaileon is built on two key innovations: In-Context Complex Co-Design (I3CD) for context-aware sequence-structure modeling, and Mixture-of-Paths Sampling (MoPS), which iteratively optimizes a shared sequence across multiple structural contexts during inference. This allows the generation of a single binder sequence that can adapt its structure to satisfy distinct binding contexts.

The team also introduced CROSS, a benchmark covering both multi-state and multi-target binder design. Extensive evaluation on CROSS demonstrates that Chamaileon effectively generates sequences adaptable to diverse conformational landscapes and multi-target requirements, opening a path toward programmable multi-specific binders and conformational modulators.

The code is available on GitHub at https://github.com/caohengyuan/Chamaileon.

Source
Hugging Face · Hugging Face Papers
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