239 citations · 473 across the 10 of their papers we have counts for
11 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…
Speak to a Protein: An Interactive Multimodal Co-Scientist
Carles Navarro, Mariona Torrens, Philipp Thölke +2
Building a working mental model of a protein typically requires weeks of reading, cross-referencing crystal and predicted structures, and inspecting ligand complexes, an effort tha…
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan Doerr +1
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand…
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…
PlayMolecule Viewer: a toolkit for the visualization of molecules and other data
Mariona Torrens-Fontanals, Panagiotis Tourlas, Stefan Doerr +1
PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design…
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke +7
A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships t…