The grand challenge in structure-based drug design is achieving accurate prediction of
binding free energies. Molecular dynamics (MD) simulations enable modeling of …

Free energy perturbation (FEP) has become widely used in drug discovery programs for
binding affinity prediction between candidate compounds and their biological targets …

The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …

Alchemical free energy simulations have long been utilized to predict free energy changes
for binding affinity and solubility of small molecules. However, while the theoretical …

Complete understanding of most, if not all chemical processes requires at its very core the
knowledge of the underlying free-energy change. In computer-aided drug design, for …

Systematic and quantitative analysis of the reliability of formally exact methods that in silico
calculate absolute protein–ligand binding free energies remains lacking. Here, we provide …

Understanding molecular mechanisms of enzymatic reactions is of vital importance in
biochemistry and biophysics. Here, we introduce new functions of hybrid quantum …

Multistate Bennett acceptance ratio (MBAR) works as a method to analyze molecular
dynamics (MD) simulation data after the simulations have been finished. It is widely used to …

Hydration free energy (HFE) is a key factor in improving protein–ligand binding free energy
(BFE) prediction accuracy. The HFE itself can be calculated using the three-dimensional …

Coarse-grained (CG) protein models have become indispensable tools for studying many
biological protein details, from conformational dynamics to the organization of protein macro …