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Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations
Stephen E. Farr, Gianni De Fabritiis
Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/m…
Acep: Thermodynamics-Informed p Prediction and Protonation-State Generation in PlayMolecule AI
Francesco Pesce, Stephen Farr, Gianni de Fabritiis
The acid dissociation constants (p) and the protonation states that they determine govern solubility, permeability, and protein--ligand binding, making their accurate pr…
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…
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…
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…