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Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \\textbf{ProtLiD$^2$}, a \\textbf{Prot}ein \\textbf{L}igand-conditioned \\textbf{D}iscrete \\textbf{D}iffusion model for protein sequence-structure co-design. ProtLiD$^2$ jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, ProtLiD$^2$ extends masked discret","title":"Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design","url":"https://arxiv.org/abs/2605.27413","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.27413v1 Announce Type: cross \nAbstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \\textbf{ProtLiD$^2$}, a \\textbf{Prot}ein \\textbf{L}igand-conditioned \\textbf{D}iscrete \\textbf{D}iffusion model for protein sequence-structure co-design. ProtLiD$^2$ jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. 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