GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913155382247424 |
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| author | Zhang, Jingjie Cao, Hanqun Shi, Haosen Mutian, He Wang, Yu Gao, Zijun Wu, Fang Yao, Xiaojun Hsieh, Chang-Yu Pan, Sinno Jialin Chatterjee, Pranam Gu, Chunbin Heng, Pheng-Ann |
| author_facet | Zhang, Jingjie Cao, Hanqun Shi, Haosen Mutian, He Wang, Yu Gao, Zijun Wu, Fang Yao, Xiaojun Hsieh, Chang-Yu Pan, Sinno Jialin Chatterjee, Pranam Gu, Chunbin Heng, Pheng-Ann |
| contents | Cyclic peptides are attractive therapeutic modalities because their closed-ring topology can improve stability and target specificity. However, de novo cyclic peptide design remains challenging for diffusion generators, as macrocyclization requires satisfying sparse, non-smooth, and compositional geometric constraints. Existing constraint-conditioned methods largely rely on inference-time guidance, which can steer samples toward desired closures but does not directly change the learned generative distribution. We propose GeoCycler, a reward-weighted diffusion alignment framework for training conditional latent diffusion models toward macrocyclization feasibility. GeoCycler introduces a type-gated stair reward that activates distance-based shaping only when prerequisite residue or linker types are satisfied, providing dense geometric feedback while avoiding misleading signals from chemically incompatible anchors. Together with positive-only reward weighting and replay-based stabilization, GeoCycler aligns a single generator across multiple cyclization topologies. On the LNR benchmark, GeoCycler improves pass@5 closure success over strong guidance-based baselines across stapled, head-to-tail, disulfide, and bicyclic settings. In particular, it improves head-to-tail success by 20.8 percentage points over CP-Composer while maintaining comparable amino-acid and backbone-dihedral statistics. These results suggest that training-time alignment to sparse geometric constraints is a promising alternative to relying solely on post hoc sampling-time correction for cyclic peptide generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_23407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design Zhang, Jingjie Cao, Hanqun Shi, Haosen Mutian, He Wang, Yu Gao, Zijun Wu, Fang Yao, Xiaojun Hsieh, Chang-Yu Pan, Sinno Jialin Chatterjee, Pranam Gu, Chunbin Heng, Pheng-Ann Computational Engineering, Finance, and Science Cyclic peptides are attractive therapeutic modalities because their closed-ring topology can improve stability and target specificity. However, de novo cyclic peptide design remains challenging for diffusion generators, as macrocyclization requires satisfying sparse, non-smooth, and compositional geometric constraints. Existing constraint-conditioned methods largely rely on inference-time guidance, which can steer samples toward desired closures but does not directly change the learned generative distribution. We propose GeoCycler, a reward-weighted diffusion alignment framework for training conditional latent diffusion models toward macrocyclization feasibility. GeoCycler introduces a type-gated stair reward that activates distance-based shaping only when prerequisite residue or linker types are satisfied, providing dense geometric feedback while avoiding misleading signals from chemically incompatible anchors. Together with positive-only reward weighting and replay-based stabilization, GeoCycler aligns a single generator across multiple cyclization topologies. On the LNR benchmark, GeoCycler improves pass@5 closure success over strong guidance-based baselines across stapled, head-to-tail, disulfide, and bicyclic settings. In particular, it improves head-to-tail success by 20.8 percentage points over CP-Composer while maintaining comparable amino-acid and backbone-dihedral statistics. These results suggest that training-time alignment to sparse geometric constraints is a promising alternative to relying solely on post hoc sampling-time correction for cyclic peptide generation. |
| title | GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2605.23407 |