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A classical virtual combinatorial chemistry approach (CombiChem) was applied for combinatorial generation of 5590 novel structurally-similar 6-fluoroquinolone analogs by using a virtual synthetic pathway with selected primary (43) and secondary amines (130). The obtained virtual combinatorial library was filtered using an in-house developed set of cheminformatics drug-likeness filters with pre-integrated Boolean options (TRUE/FALSE) for compounds reduction/selection. The retained number (304) of fluoroquinolone analogs (with TRUE outcome) defines the drug-like chemical space (CombiData). Quantitative structure-activity relationships (QSAR) study on these 304 virtually generated 6-fluoroquinolone analogs with unknown activity values was performed using a pre-built five-parameter multiple linear regression (MLR) model developed on a set of compounds with experimentally determined activity values (Rtr = 0.8417, Rtr-cv = 0.7884). The obtained activity values for the unknown compounds together with the model results were used to define the applicability domain (AD). The obtained AD offers a good graphical representation and establishment of structure-activity relationships (SAR) which could be used for design of new 6-fluoroquinolones with possible better activity.

Chemometrical Exploration of Combinatorially Generated Drug-like Space of 6-fluoroquinolone Analogs: A QSAR Study.