theoretical methods are making simulations come increasingly closer to predicting ligand binding affinities with pharmaceutical accuracy. This level of precision makes it possible to separate errors caused by poor sampling from the inadequacies of molecular mechanics force fields. These improvements are beginning to change the process of designing drugs with high affinities and specific action from a trial-and-error art to a nanoscale engineering process.
However, current computational methods are inefficient and converge relatively slowly, requiring computational resources that far exceed what is currently cost-effective in the pharmaceutical industry. Extended ensemble methods can significantly increase the speed at which simulations sample macromolecular conformations by including Monte Carlo transitions between nonphysical states , and provide some hope for improving efficiency.
We compare extended ensemble simulations of ligand binding affinities and more standard ligand binding methods for the FKBP-12 system in order to understand how much more efficient such simulations may be. Using the same methods, we also examine the relative binding affinity of over 50 ligands bound to Factor Xa and use this data to rationalize
different structural determinants of binding affinity . We also look at the effect of different charge models for ligands on the binding affinities, observing variances of up to 0.5-1.0 kcal/mol when the same charge determination method is applied to different input ligand structures, putting an upper bound on the accuracy of atomistic simulations using classical fixed charge force fields.
 B. Roux and J. Faraldo-Gomez, J. Comput. Chem. 28:1634-1647 (2007)
 T. Young, R. Abel, B. Kim, B. J. Berne, and R. A. Friesner, Proc. Nat. Acad. Sci. 104:808-813 (2007)