AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data.

It has been notoriously difficult to develop general all-purpose scoring functions for high-throughput docking that correlate with measured binding affinity. As a practical alternative, AutoShim uses the program Magnet to add point-pharmacophore like “shims” to the binding site of each protein target. The pharmacophore shims are weighted by partial least-squares (PLS) regression, adjusting the all-purpose scoring function to reproduce IC50 data, much as the shims in an NMR magnet are weighted to optimize the field for a better spectrum. This dramatically improves the affinity predictions on 25% of the compounds held out at random. An iterative procedure chooses the best pose during the process of shim parametrization. This method reproducibly converges to a consistent solution, regardless of starting pose, in just 2−4 iterations, so these robust models do not overtrain. Sets of complex multifeature shims, generated by a recursive partitioning method, give the best activity predictions, but these are diffi...