Abstract
In general, the docking scoring tends to have a size dependence related to the ranking of compounds. In this paper, we describe a novel method of parameter optimization for docking scores which reduce the size dependence and can efficiently discriminate active compounds from chemical databases. This method is based on a simplified theoretical model of docking scores which enables us to utilize large amounts of data of known active and inactive compounds for a particular target without requiring large computational resources or a complicated procedure. This method is useful for making scoring functions for the identification of novel scaffolds using the knowledge of active compounds for a particular target or a customized scoring function for an interesting family of drug targets.
Acknowledgments
This work was supported by the National Institute of Biomedical Innovation and the Drug Discovery Program of the Pharma Valley Center.