1 citations · 1 across the 1 of their papers we have counts for
5 papers
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
Stephen E. Farr, Stefan Doerr, Antonio Mirarchi +2
We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to D…
Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez +2
Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design.…
mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics
Antonio Mirarchi, Toni Giorgino, Gianni De Fabritiis
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive data…
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon +1
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological process…
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis +6
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents subs…