23, 26-28 , however, with similar inhibition concentration ranges. Most probably the limited volume, the hydrophobicity and the steric constrains of the idc binding-cavity, could explain the similar antibacterial inhibitor concentrations of alternative drugs . Cocrystal FtsZ complexes with idc ligands, included similar hydrogen bonds (V 207 , L 209 , N 263 ), and main hydrophobic interactions (T 309 ,G 196 ) than those observed for FtsZ-PC190723 complexes (i.e.,4dxd). PC190723-resistant bacterial mutations confirmed the importance in binding of some of the amino acid residues mentioned above such as G 196 and T 309 . On the other hand, an empty cavity implicating residues M 226 , I 228 , Q 192 and L 249 extends above and behind the idc 9, , similarly to the one described for the Taxol binding site in human ß-tubulin 29 , 6, 10 perhaps waiting for yet unexplored ligands 2 . Previous attempts to find new FtsZ idc docking candidates by computational screening of Zinc, MolPort or Mcule libraries containing millions-of compounds, identified ~ 100 new ligands with experimental inhibition of bacterial cell division, but only at low µM ranges . The main objective of computational docking is to predict conformational poses of each ligand into the binding-cavity of a targeted protein and accurately ranking them according to their affinity. However, no single docking algorithm predicts the same conformational poses or docking-scores with high accuracy respect to their corresponding binding affinities. Actually, consensus docking may be one of the best, yet limited, approach to increase binding predictions. The first consensus averaging docking-scores by several algorithms, had poor accuracies predicting known benchmarks. Alternative methods using ranks rather than scores outperform the averaged results . Consensus combining conformational pose and ranking approaches from several programs (i.e., AutoDockVina, rDock, AutoDock4, PLANTS) improved their isolated performances . Similar consensus have been proposed using exponential consensus and RMSD ranks like DockECR () , or DockingPie () . Those strategies employed different algorithms one-by-one and their results were then pooled into different calculation methods to quantify a consensus parameter. Despite benchmark consensus improvements in docking and conformation accuracies, docking-scores and their ranking remain challenging for large screenings.