Raimondas Galvelis

According to our database, Raimondas Galvelis authored at least 9 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials.

Francesc Sabanés Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis

J. Chem. Inf. Model., March, 2024

On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials.

Guillem Simeon, Antonio Mirarchi, Raúl P. Peláez, Raimondas Galvelis, Gianni De Fabritiis

CoRR, 2024

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Raúl P. Peláez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter K. Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis

CoRR, 2024

2023

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

J. Chem. Inf. Model., September, 2023

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Peter K. Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João P. G. L. M. Rodrigues, Jaime Rodríguez-Guerra, Andrew C. Simmonett, Jason M. Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland

CoRR, 2023

2022

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.

Peter K. Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, Thomas E. Markland

CoRR, 2022

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics.

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

CoRR, 2022

2019

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Raimondas Galvelis, Stefan Doerr, João M. Damas, Matt J. Harvey, Gianni De Fabritiis

J. Chem. Inf. Model., 2019

2015

Replica state exchange metadynamics for improving the convergence of free energy estimates.

Raimondas Galvelis, Yuji Sugita

J. Comput. Chem., 2015