SecA ATPase plays a crucial role in translocation of membrane and secreted polypeptides and proteins in bacteria
and therefore a perfect target for novel antimicrobial drug design. Herein, we generated QSAR models with an
alignment-independent method. The optimum model obtained for the training set was statistically significant with crossvalidation
regression coefficient (q2) value of 0.40 and correlation coefficient (r2) value of 0.89. These results suggest that
this 3D-QSAR model can be used to guide the development of new SecA inhibitors.
Keywords: SecA ATPase, inhibitors, antimicrobial drug, 3D-QSAR.
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