Abstract:D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating D-peptide binders remains largely unexplored. In this work, we show that by injecting axial features to $E(3)$-equivariant (polar) vector features, it is feasible to achieve cross-chirality generalization from homo-chiral (L--L) training data to hetero-chiral (D--L) design tasks. By implementing this method within a latent diffusion model, we achieved D-peptide binder design that not only outperforms existing tools in \textit{in silico} benchmarks, but also demonstrates efficacy in wet-lab validation. To our knowledge, our approach represents the first wet-lab validated generative AI for the \textit{de novo} design of D-peptide binders, offering new perspectives on handling chirality in protein design. Codes are available at this https URL
From: Zitong Tian [view email]
[v1]
Fri, 13 Feb 2026 02:46:29 UTC (31,512 KB)
[v2]
Thu, 28 May 2026 07:16:41 UTC (14,121 KB)
[v3]
Fri, 29 May 2026 08:35:00 UTC (14,120 KB)
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