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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.04225 |
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| _version_ | 1866915387784822784 |
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| author | Jiang, Dapeng Kong, Xiangzhe Han, Jiaqi Li, Mingyu Jiao, Rui Huang, Wenbing Ermon, Stefano Ma, Jianzhu Liu, Yang |
| author_facet | Jiang, Dapeng Kong, Xiangzhe Han, Jiaqi Li, Mingyu Jiao, Rui Huang, Wenbing Ermon, Stefano Ma, Jianzhu Liu, Yang |
| contents | Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical needs. However, designing target-specific cyclic peptides remains underexplored due to limited training data. To bridge the gap, we propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation via composable geometric constraints. Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model through geometric conditioning on nodes and edges. During training, the model learns from unit constraints and their random combinations in linear peptides, while at inference, novel constraint combinations required for cyclization are imposed as input. Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints Jiang, Dapeng Kong, Xiangzhe Han, Jiaqi Li, Mingyu Jiao, Rui Huang, Wenbing Ermon, Stefano Ma, Jianzhu Liu, Yang Machine Learning Artificial Intelligence Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical needs. However, designing target-specific cyclic peptides remains underexplored due to limited training data. To bridge the gap, we propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation via composable geometric constraints. Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model through geometric conditioning on nodes and edges. During training, the model learns from unit constraints and their random combinations in linear peptides, while at inference, novel constraint combinations required for cyclization are imposed as input. Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies. |
| title | Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04225 |