Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

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 …

We introduce the Open Force Field (OpenFF) 2.0. 0 small molecule force field for drug-like
molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF …

Computational techniques can speed up the identification of hits and accelerate the
development of candidate molecules for drug discovery. Among techniques for predicting …

We present a methodology for defining and optimizing a general force field for classical
molecular simulations, and we describe its use to derive the Open Force Field 1.0. 0 small …

This letter gives results on improving protein–ligand binding affinity predictions based on
molecular dynamics simulations using machine learning potentials with a hybrid neural …

Abstract While Relative Binding Free Energy (RBFE) calculations have become a mainstay
in lead optimization programs, the computational expense of performing these calculations …

The development of accurate transferable force fields is key to realizing the full potential of
atomistic modeling in the study of biological processes such as protein–ligand binding for …

Nowadays, drug design projects benefit from highly accurate protein–ligand binding free
energy predictions based on molecular dynamics simulations. While such calculations have …