Analysis and optimization of structure-based virtual screening protocols. (3). New methods and old problems in...

Scoring function research remains a primary focus of current structure-based virtual screening (SVS) technology development. Here, we present an alternative method for scoring function design that attempts to combine crystallographic structural information with data derived from directly within SVS calculations. The technique utilizes a genetic algorithm (GA) to optimize functions based on binding property data derived from multiple virtual screening calculations. These calculations are undertaken on protein data bank (PDB) complex active sites using ligands of known binding mode in conjunction with "noise" compounds. The advantages of such an approach are that the function does not rely on assay data and that it can potentially use the "noise" binding data to recognize the sub-optimal docking interactions inherent in SVS calculations. Initial efforts in technique exploration using DOCK are presented, with comparisons made to existing DOCK scoring functions. An analysis of the problems inherent to scoring function development is also made, including issues in dataset creation and limitations in descriptor utility when viewed from the perspective of docking mode resolution. The future directions such studies might take are also discussed in detail.