arXiv:2602.11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties. This task is challenging because a cyclic peptide often exhibits diverse, ring-shaped conformations, which cannot be well captured by deterministic prediction models derived from linear peptide folding. In this study, we propose MuCO (Multi-stage Conformation Optimization), a generative peptide cyclization method that models the distribution of cyclic peptide conformations conditioned on the cor
The increasing sophistication of AI models, particularly in generative approaches, now enables more complex molecular modeling tasks like peptide cyclization.
This breakthrough advances the ability to design peptides with precise properties, critical for drug discovery and material science, impacting pharmaceutical development and therapeutic design.
Traditional deterministic models are insufficient for complex peptide structures; generative AI offers a new paradigm for accurately modeling diverse conformations of cyclic peptides.
- · Pharmaceutical R&D
- · Biotechnology sector
- · AI for drug discovery companies
- · Traditional drug discovery methods
- · Companies without AI integration
Accelerated discovery and development of novel peptide-based drugs and therapies.
Increased efficiency and reduced costs in preclinical drug development, leading to faster market entry for new treatments.
The ability to design entirely new classes of biomaterials and therapeutics with unprecedented specificity and efficacy, potentially revolutionizing medicine and materials science.
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Read at arXiv cs.AI