A dominant assumption in pharmacology throughout the 20th century has been that in vivo
target occupancy—and attendant pharmacodynamics—depends on the systemic …

The prediction of drug–target binding and unbinding kinetics that occur on time scales
between milliseconds and several hours is a prime challenge for biased molecular …

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes
in atomic modeling, simulation, and design. AI-driven potential energy models have …

Identifying a reduced set of collective variables is critical for understanding atomistic
simulations and accelerating them through enhanced sampling techniques. Recently …

Recent success stories suggest that in silico protein–ligand binding free-energy calculations
are approaching chemical accuracy. However, their widespread application remains limited …

The rapid development of artificial intelligence (AI) is driving significant changes in the field
of atomic modeling, simulation, and design. AI-based potential energy models have been …

Molecular simulations are an essential asset in the first steps of drug design campaigns.
However, the requirement of high-throughput limits applications mainly to qualitative …

Early‐stage drug discovery projects often focus on equilibrium binding affinity to the target
alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are …

Computational techniques applied to drug discovery have gained considerable popularity
for their ability to filter potentially active drugs from inactive ones, reducing the time scale …

Ligand unbinding is mediated by its free energy change, which has intertwined contributions
from both energy and entropy. It is important, but not easy, to quantify their individual …