58 citations · 113 across the 5 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 +4
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…
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…
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…
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.…
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…