3 papers
q-bio.BM2026
PHASE: encoding global protein ensembles with local Hamiltonians and all-atom backmapping
Daniele Angioletti, Marco Nobile, Matteo Carli +1
Protein function is governed by conformational ensembles, which can be viewed as high-dimensional probability distributions over molecular conformations. Yet the statistical organi…
cs.LG2026
GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
Daniele Angioletti, Marco Nobile, Vittorio Limongelli
Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific t…
physics.chem-ph2024
HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations
Daniele Angioletti, Stefano Raniolo, Vittorio Limongelli
Molecular simulations have assumed a paramount role in the fields of chemistry, biology, and material sciences, being able to capture the intricate dynamic properties of systems. W…