Author

Daniele Angioletti

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Review Open access Sep 2026

Deep Learning for Protein Modeling: From Single Structure to Bound Complex and Thermodynamic Ensemble, With a Focus on Architectural Design

Generative kinetics is indicated as an aspirational frontier in building physically grounded and transferable models, including data availability and fidelity, the choice of inductive biases to pursue generalization, and the need for rigorous model evaluation.

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

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