Stephen E. Farr

collaborators

4 papers

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

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…

physics.chem-ph2025

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…

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

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials

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