3 citations · 6 across the 5 of their papers we have counts for
5 papers
Binding Free Energies without Alchemy
Michael Brocidiacono, Brandon Novy, Rishabh Dey +2
Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by t…
Extending machine learning model for implicit solvation to free energy calculations
Rishabh Dey, Michael Brocidiacono, Kushal Koirala +2
The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to…
Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Michael Brocidiacono, James Wellnitz, Konstantin I. Popov +1
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for nov…
An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Michael Brocidiacono, Konstantin I. Popov, Alexander Tropsha
Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over deco…
PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking
Michael Brocidiacono, Konstantin I. Popov, David Ryan Koes +1
Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scori…