mlsanityData to 7 Oct 2026

Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design

arXiv 2602.20176 ↗First posted 13 Feb 2026q-bio.BM cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating D-peptide binders remains largely unexplored. In this work, we show that by injecting axial features to E(3)-equivariant (polar) vector features, it is feasible to achieve cross-chirality generalization from homo-chiral (L--L) training data to hetero-chiral (D--L) design tasks. By implementing this method within a latent diffusion model, we achieved D-peptide binder design that not only outperforms existing tools in textitin silico benchmarks, but also demonstrates efficacy in wet-lab validation. To our knowledge, our approach represents the first wet-lab validated generative AI for the textitde novo design of D-peptide binders, offering new perspectives on handling chirality in protein design. Codes are available at https://github.com/YZY010418/PepMirror

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 49 references that are in the map. First posted on arXiv 13 Feb 2026.

Builds on

15 of 49 references.

References · 15

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2021E(n) Equivariant Graph Neural Networkscited by 951
  3. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  4. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  5. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  6. 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
  7. 2020Finding Symmetry Breaking Order Parameters with Euclidean Neural Networkscited by 30
  8. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  9. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  10. 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
  11. 2022Geometry-Complete Perceptron Networks for 3D Molecular Graphscited by 15
  12. 2020Message Passing Networks for Molecules with Tetrahedral Chiralitycited by 14
3 more
  1. 2025E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Productscited by 12
  2. 2025UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Designcited by 11
  3. 2025Unified all-atom molecule generation with neural fieldscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding
  2. 2026Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design
  3. 2026FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
  4. 2025Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks
  5. 2025THFlow: A Temporally Hierarchical Flow Matching Framework for 3D Peptide Design
  6. 2025Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
  7. 2026PepEDiff: Zero-Shot Peptide Binder Design via Protein Embedding Diffusion
  8. 2025PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning