The significant role played by docking algorithms in drug discovery combined with their serious pitfalls prompted us to envisage a novel concept for validating docking solutions, namely, docking-based comparative intermolecular contacts analysis (dbCICA). This novel approach is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration on the basis of its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands and vice versa. In other words, dbCICA evaluates the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models can be translated into valid pharmacophore models that can be used as 3-D search queries to mine structural databases for new bioactive compounds. dbCICA was implemented to search for new inhibitors of candida N-myristoyl transferase as potential antifungal agents and glycogen phosphorylase (GP) inhibitors as potential antidiabetic agents. The process culminated in five selective micromolar antifungal leads and nine GP inhibitory leads.

Check point kinase 1 (Chk1) is an important protein in G2 phase checkpoint arrest required by cancer cells to maintain cell cycle and to prevent cell death. Therefore, Chk1 inhibitors should have potential as anti-cancer therapeutics. Docking-based comparative intermolecular contacts analysis (dbCICA) is a new three-dimensional quantitative structure activity relationship method that depends on the quality and number of contact points between docked ligands and binding pocket amino acid residues. In this presented work we implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling coupled with dbCICA to select critical ligand-Chk1 contacts capable of explaining anti-Chk1 bioactivity among a long list of inhibitors. The finest set of contacts were translated into two valid pharmacophore hypotheses that were used as 3D search queries to screen the National Cancer Institute's structural database for new Chk1 inhibitors. Three potent Chk1 inhibitors were discovered with IC50 values ranging from 2.4 to 69.7 µM.

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.

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