The application of computational approaches in drug discovery has been consolidated in
the last decades. These families of techniques are usually grouped under the common …

This Perspective provides a contextual explanation of the current state-of-the-art alchemical
free energy methods and their role in drug discovery as well as highlights select emerging …

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 …

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free
energies (RBFEs) of a diverse set of protein–ligand complexes. We employed a streamlined …

Binding free energy calculations predict the potency of compounds to protein binding sites in
a physically rigorous manner and see broad application in prioritizing the synthesis of novel …

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

Introduction The size and complexity of virtual screening libraries in drug discovery have
skyrocketed in recent years, reaching up to multiple billions of accessible compounds …

The de novo design of small molecule–binding proteins has seen exciting recent progress;
however, high-affinity binding and tunable specificity typically require laborious screening …

Alchemical free-energy methods based on molecular dynamics (MD) simulations have
become important tools to identify modifications of small organic molecules that improve …

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the
intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The …