Author

V. Limongelli

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Preprint Aug 2026

PHASE: encoding global protein ensembles with local Hamiltonians and all-atom backmapping

PHASE (Protein Hamiltonians for Sampling of Ensembles), a system-specific framework that converts atomistic conformational ensembles into an explicit and interpretable statistical model, is introduced.

Daniele Angioletti, Marco S. Nobile, Matteo Carli et al. · 0 citations

#machine learning Preprint Jul 2026

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

GEqTrain is presented, a configuration-driven framework that separates dataset semantics, model composition, and training objectives, and GEqDiff, a generative extension based on equivariant flow matching that aims to make equivariant modeling more reproducible, extensible, and reusable.

Daniele Angioletti, Marco Nobile, V. Limongelli · 0 citations