Drug-target residence time(t=1/koff,koff is the dissociation rate constant)has become an important index in discovering better-or best-in-class drugs.However,little effort has been dedicated to develop computational methods that can accurately predict this kinetic parameter or related parameters,koff and activation free energy of dissociation().Recently,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(BFEL).This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding events of the anti-Alzheimers disease drugs(-)-Huperzine A(HupA)and E2020 to the target acetylcholinesterase(AChE).The computational results are in excellent agreement with our concurrent experimental measurements.All the predicted values of binding free energy and activation free energies of association and dissociation only deviate from the experimental data by less than 2 kcal/mol.The method also provides atomic resolution information for the ligand binding pathways,which may be useful in designing more potent AChE inhibitors.We expect this methodology to be widely applicable to drug discovery and development.