Latent Retrieval Augmented Generation of Cross-Domain Protein Binders

• URL: http://arxiv.org/abs/2510.10480v2
• Date: Thu, 16 Oct 2025 11:55:27 GMT
• Title: Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
• Abstract summary: We propose Retrieval-Augmented Diffusion for Aligned interface (RADiAnce) to guide the design of novel protein binders.<n>By unifying retrieval and generation in a shared contrastive latent space, our model efficiently identifies relevant interfaces for a given binding site.<n>Our work establishes a new paradigm for protein binder design that successfully bridges retrieval-based knowledge and generative AI.
• Score: 22.891733948881512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing protein binders targeting specific sites, which requires to generate realistic and functional interaction patterns, is a fundamental challenge in drug discovery. Current structure-based generative models are limited in generating nterfaces with sufficient rationality and interpretability. In this paper, we propose Retrieval-Augmented Diffusion for Aligned interface (RADiAnce), a new framework that leverages known interfaces to guide the design of novel binders. By unifying retrieval and generation in a shared contrastive latent space, our model efficiently identifies relevant interfaces for a given binding site and seamlessly integrates them through a conditional latent diffusion generator, enabling cross-domain interface transfer. Extensive exeriments show that RADiAnce significantly outperforms baseline models across multiple metrics, including binding affinity and recovery of geometries and interactions. Additional experimental results validate cross-domain generalization, demonstrating that retrieving interfaces from diverse domains, such as peptides, antibodies, and protein fragments, enhances the generation performance of binders for other domains. Our work establishes a new paradigm for protein binder design that successfully bridges retrieval-based knowledge and generative AI, opening new possibilities for drug discovery.

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