58 citations · 95 across the 7 of their papers we have counts for
7 papers
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.…
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…
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…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson +9
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe…
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr +5
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational…
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning
Raimondas Galvelis, Stefan Doerr, Joao M. Damas +2
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FFs are not generally available for all molecules, like novel druglike molecule…