PepCompass: Navigating Peptide Embedding Spaces Using Riemannian Geometry

MCML Authors

Michal Kmicikiewicz

→ Group Vincent Fortuin
 Bayesian Deep Learning

Abstract

Antimicrobial peptide discovery is challenged by the astronomical size of peptide space and the relative scarcity of active peptides. While generative models provide latent maps of this space, they typically ignore decoder-induced geometry and rely on flat Euclidean metrics, making exploration distorted and inefficient. Existing manifold-based approaches assume fixed intrinsic dimensionality, which fails for real peptide data. We introduce PepCompass, a geometry-aware framework based on a Union of K-Stable Riemannian Manifolds that captures local decoder geometry while maintaining computational stability. PepCompass performs global interpolation via Potential-minimizing Geodesic Search (PoGS) to bias discovery toward promising seeds and enables local exploration through Second-Order Riemannian Brownian Efficient Sampling and Mutation Enumeration in Tangent Space, which together form Local Enumeration Bayesian Optimization (LE-BO). PepCompass achieves a 100% in-vitro validation rate: PoGS identifies four novel seeds and LE-BO optimizes them into 25 highly active, broad-spectrum peptides, demonstrating that geometry-informed exploration is a powerful paradigm for antimicrobial peptide design.

inproceedings MBP+26

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ICML 2026

43rd International Conference on Machine Learning. Seoul, South Korea, Jul 06-11, 2026. To be published. Preprint available.

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Authors

M. Możejko • A. Bielecki • J. Prądzyński • H.-S. Lee • A. Janowski • M. Kmicikiewicz • P. Szymczak • K. Jurasz • M. Traskowski • M. Kucharczyk • M. Der Torossian Torres • C. de la Fuente-Nunez • E. Szczurek

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Links

URL GitHub

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Research Area

 A1 | Statistical Foundations & Explainability