Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints

• URL: http://arxiv.org/abs/2507.04225v2
• Date: Mon, 14 Jul 2025 14:33:47 GMT
• Title: Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
• Authors: Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu,
• Abstract summary: We propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation.<n>Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model.<n>Our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84%.
• Score: 65.77915791312634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation [79.27003481818413]
  We introduce FlatVI, a training framework that regularises the latent manifold of discrete-likelihood variational autoencoders towards Euclidean geometry.<n>By encouraging straight lines in the latent space to approximate geodesics on the decoded single-cell manifold, FlatVI enhances compatibility with downstream approaches.
  arXiv  Detail & Related papers  (2025-07-15T23:08:14Z)
• CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization [51.716834831684004]
  We study the problem of conformal prediction (CP) under geometric data shifts.<n>We propose integrating geometric information--such as geometric pose--into the conformal procedure to reinstate its guarantees.
  arXiv  Detail & Related papers  (2025-06-19T10:12:02Z)
• Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling [58.384011300570585]
  We introduce CpSDE, which consists of two key components: AtomSDE, a generative structure prediction model based on harmonic SDE, and Res, a residue type predictor.<n>CpSDE overcomes existing data limitations and is proficient in designing a wide variety of cyclic peptides.<n>Our experimental results demonstrate that the cyclic peptides designed by our method exhibit reliable stability and affinity.
  arXiv  Detail & Related papers  (2025-05-27T17:24:12Z)
• CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization [19.795752582745397]
  Target-specific peptides, such as conotoxins, exhibit exceptional binding affinity and selectivity toward ion channels and receptors.<n>Here, we present CreoPep, a deep learning-based conditional generative framework that integrates masked language modeling with a progressive masking scheme to design high-affinity peptide mutants.<n>We validate this approach by designing conotoxin inhibitors targeting the $alpha$7 nicotinic acetylcholine receptor, achieving submicromolar potency in electrophysiological tests.
  arXiv  Detail & Related papers  (2025-05-05T15:56:39Z)
• PPFlow: Target-aware Peptide Design with Torsional Flow Matching [52.567714059931646]
  We propose a target-aware peptide design method called textscPPFlow to model the internal geometries of torsion angles for the peptide structure design.<n>Besides, we establish a protein-peptide binding dataset named PPBench2024 to fill the void of massive data for the task of structure-based peptide drug design.
  arXiv  Detail & Related papers  (2024-03-05T13:26:42Z)
• Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion [0.5475672579692472]
  RINGER is a diffusion-based transformer model that generates 3D conformational ensembles of macrocyclic peptides from their 2D representations. We show how RINGER generates both high-quality and diverse geometries at a fraction of the computational cost.
  arXiv  Detail & Related papers  (2023-05-30T16:39:18Z)
• CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning [0.1747623282473278]
  We introduce CREMP, a resource for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST) Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations.
  arXiv  Detail & Related papers  (2023-05-14T03:50:46Z)
• Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning [54.247560894146105]
  Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. We propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity.
  arXiv  Detail & Related papers  (2022-08-10T13:30:58Z)
• Entropic trust region for densest crystallographic symmetry group packings [0.8399688944263843]
  Molecular crystal structure prediction seeks the most stable periodic structure given the chemical composition of a molecule and pressure-temperature conditions. Modern CSP solvers use global optimization methods to search for structures with minimal free energy within a complex energy landscape induced by intermolecular potentials. We propose a class of periodic packings restricted to crystallographic symmetry groups (CSG) and design a search method for the densest CSG packings in an information-geometric framework.
  arXiv  Detail & Related papers  (2022-02-24T08:39:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.