Overview of the SAMPL5 Host-Guest Challenge: Are We Doing Better? Jian Yin†, Niel M. Henriksen†, David R. Slochower†, Michael R. Shirts‡, Michael W. ¶ † * Chiu , David L. Mobleyѱ and Michael K. Gilson †Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA ‡Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia 22904, USA ¶Qualcomm Institute, University of California, San Diego, La Jolla, CA 92093, USA ѱDepartments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, CA 92697, USA Keywords Host-guest, molecular recognition, computer-aided drug design, Blind challenge, Binding affinity *Corresponding author: Prof. Michael K. Gilson Email:
[email protected] Telephone: 858-822-0622 Fax: 858-822-7726 Abstract The ability to computationally predict protein-small molecule Binding affinities with high accuracy would accelerate drug discovery and reduce its cost By eliminating rounds of trial-and- error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can Be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to oBtain well-converged results and to trouBleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition But are far smaller and simpler. As a consequence, host-guest systems have Been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to Being Blinded, and thus avoiding Biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may Be compared and ideas exchanged.