arXiv:2605.27413v1 Announce Type: cross Abstract: 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 \
The accelerating pace of AI research convergence with biological sciences is enabling more sophisticated computational tools for fundamental protein design, overcoming prior limitations in small-molecule conditioning.
Advanced protein co-design, explicitly incorporating ligand interactions, is critical for developing next-generation therapeutics, enzymes, and biomaterials with precise functions.
The ability to co-design protein sequences and structures under explicit ligand constraints opens new avenues for rational drug discovery and synthetic biology, moving beyond trial-and-error methods.
- · Biopharmaceutical industry
- · Synthetic biology companies
- · AI-driven drug discovery platforms
- · Academic research institutions
- · Traditional drug discovery methods
- · Companies reliant on broad-spectrum compounds
More efficient and targeted drug development processes for previously intractable diseases.
Reduced R&D costs and faster time-to-market for novel protein-based therapeutics and industrial enzymes.
The creation of entirely new classes of programmable biological machines and materials with unprecedented capabilities.
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Read at arXiv cs.AI