Could DW docking-scores, ligand efficiency and consensus ADV docking improve FtsZ binding predictions? Experimental screening for binding affinity may be a valid strategy to identify antibacterials since inhibition correlated with FtsZ binding affinities better than with either FtsZ polymerization or GTPase assays 10, 11 . However, to predict experimental binding is challenging due to the limitations of present in silico docking algorithms, such as DW and ADV. Known limitations include fixing of the docking-cavity amino acid side-chains, eliminating water molecules, unreliable calculation of docking-scores, molecular geometry changes by force-field energy minimizations , and unreachable chemotype/chemical spaces 38, 39 . Specially the force-field failures to correctly recognize different atom types, would greatly affect the conservation of molecular geometries after minimization . Additionally, in the absence of better crystallographic data, the 4dxd crystallographic FtsZ model used here has no K + , no Mg ++ and GDP instead of GTP, all of which may not completely reflect the molecular environment for docking. The work described here have also other limitations due to the number of options discarded during screening (i.e., arbitrary selected PC190723 fragments, other multiple fitting criteria, etc). Additionally, known experimental alternatives that could be computationally mimicked to reduce hydrophobicity have not been explored (i.e., benzene mimics , non-flat fragment or phosphate additions, amino acid ester or glycerol extensions, and many other known pro-drug alternatives ). Furthermore, other FtsZ docking cavities or FtsZ relaxed monomer conformations have not been targeted and possible out-targeting to other proteins has not been tested (i.e, human tubulins and abundant proteins in human serum). Although bacterial FtsZ are structurally similar to eukaryotic tubulins and share their GTPase activities, eukaryotic tubulin ligands do not usually bind FtsZ, and FtsZ ligands do not bind bacterial tubulins, but testing for possible interferences should be included. It may be also expected that during binding, flexibility of the cavity side chains as well as movement of the ligands, could add alternative binding cavities depending of environmental variables (i.e. pH, temperature, ionic concentrations, etc). Although Molecular Dynamic simulations could help to discard or favor some of the complexes, their reliability is still dependent of force-field inaccuracies . Although experimental bacterial inhibitions were proportional to FtsZ bindings, lower docking-score ligands, may not predict higher biological inhibitions. Molecular weight and hydrophobicity may also influence the biological activity, despite a low docking-score. During screening and selection processes based only in docking-score ranks, the candidates increased in molecular weight and hydrophobicity resulting in trends to unspecific leads as demonstrated in many other systems 40, 41 . Among the many parameters proposed to correct for such unspecificities, the LELP parameter was selected here to monitor docking-scores because it reflects in one unique parameter both molecular weight and hydrophobicity unspecificities . Ligand efficiency ranks were applied to the lead screening and selection process in the hope to improve their accuracies, but experimental evidence should be performed to validate these efforts.