Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

We describe a general methodology for designing an empirical
scoring
function and provide smina, a version of AutoDock Vina specially optimized
to support high-throughput scoring and user-specified custom scoring
functions. Using our general method, the unique capabilities of smina,
a set of default interaction terms from AutoDock Vina, and the CSAR
(Community Structure–Activity Resource) 2010 data set, we created
a custom scoring function and evaluated it in the context of the CSAR
2011 benchmarking exercise. We find that our custom scoring function
does a better job sampling low RMSD poses when crossdocking compared
to the default AutoDock Vina scoring function. The design and application
of our method and scoring function reveal several insights into possible
improvements and the remaining challenges when scoring and ranking
putative ligands.