AutoShim: Empirically corrected scoring functions for quantitative docking with a crystal structure and IC(50) training data

作者：Martin Eric J^*; Sullivan David C

来源：Journal of Chemical Information and Modeling, 2008, 48(4): 861-872.

DOI：10.1021/ci7004548

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 IC(50) 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 difficult to interpret when designing new compounds. Sets of simpler single-point pharmacophores still predict affinity reasonably well and clearly indicate the molecular interactions producing effective binding. The pharmacophore requirements are very reproducible, irrespective of the compound sets used for parametrization, lending confidence to the predictions and interpretations. The automated procedure does require a training set of experimental compounds but otherwise adds little extra time over conventional docking.

• 出版日期2008-4