Drug discovery aims to select proper targets and drug candidates to address unmet clinical
needs. The end-to-end drug discovery process includes all stages of drug discovery from …

The biological function of proteins is determined not only by their static structures but also by
the dynamic properties of their conformational ensembles. Numerous high-accuracy static …

Accurate prediction of protein–ligand binding affinity is pivotal for drug design and discovery.
Here, we proposed a novel deep fusion graph neural networks framework named FGNN to …

Diabetes mellitus is known as one of the most life-threatening diseases and has attracted
the attention of medicinal chemists. The design and synthesis of novel potential candidates …

Abstract Dual falcipain-2 (FP-2) and falcipain-3 (FP-3) inhibitors, NM12 and NM15,
displayed micromolar inhibitions but they exhibit similar binding affinities for the human …

The development of machine-learning (ML) potentials offers significant accuracy
improvements compared to molecular mechanics (MM) because of the inclusion of quantum …

We present a comprehensive study investigating the potential gain in accuracy for
calculating absolute solvation free energies (ASFE) using a neural network potential to …

Accuracy of binding free energy calculations utilizing implicit solvent models is critically
affected by parameters of the underlying dielectric boundary, specifically, the atomic and …

Here, we report the accuracy improvements of molecular mechanics Poisson–Boltzmann
surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM …

The COVID-19 pandemic has caused havoc around the globe since 2019 and is considered
the largest global epidemic of the twentieth century. Although the first antiviral drug …