Stephen E. Farr

activity

20232026

most citedOpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

20 citations · 21 across the 5 of their papers we have counts for

collaborators

Showing 2026Show all

3 papers · 1 filter

physics.chem-ph2026

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…

physics.chem-ph2026

Acep $K_{a}$ : Thermodynamics-Informed p $K_{a}$ Prediction and Protonation-State Generation in PlayMolecule AI

Francesco Pesce, Stephen Farr, Gianni de Fabritiis

The acid dissociation constants (p $K_{a}$ ) and the protonation states that they determine govern solubility, permeability, and protein--ligand binding, making their accurate pr…

physics.chem-ph2026★ 1 cited

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…

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations

AcepKa​: Thermodynamics-Informed pKa​ Prediction and Protonation-State Generation in PlayMolecule AI

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules

Acep $K_{a}$ : Thermodynamics-Informed p $K_{a}$ Prediction and Protonation-State Generation in PlayMolecule AI