CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and they are unable to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample chemical relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.