Drug-target residence time (t = 1/koff, where koff is the dissociation
rate constant) has become an important index in discovering betteror
best-in-class drugs. However, little effort has been dedicated to
developing computational methods that can accurately predict this
kinetic parameter or related parameters, koff and activation free
energy of dissociation (ΔG≠
off). In this paper, energy landscape theory
that has been developed to understand protein folding and function
is extended to develop a generally applicable computational framework
that is able to construct a complete ligand-target binding free
energy landscape. This enables both the binding affinity and the
binding kinetics to be accurately estimated.We applied this method
to simulate the binding event of the anti-Alzheimer’s disease drug
(−)−Huperzine A to its target acetylcholinesterase (AChE). The computational
results are in excellent agreement with our concurrent
experimental measurements. All of the predicted values of binding
free energy and activation free energies of association and dissociation
deviate from the experimental data only by less than 1 kcal/
mol. The method also provides atomic resolution information for the
(−)−Huperzine A binding pathway, which may be useful in designing
more potent AChE inhibitors. We expect thismethodology to be
widely applicable to drug discovery and development
