QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus
Abstract
Background: Staphylococcus aureus bacterial infections are still a serious health care problem. Therefore, the development of new drugs for these infections is a constant requirement.
Quantitative structure-activity relationship (QSAR) methods can assist this development. Methods: The study included 151 structurally diverse compounds with antibacterial activity against S. aureus ATCC 25923 (Endpoint 1) or the drug-resistant clinical isolate of S. aureus (Endpoint 2).
QSARs based on hybrid optimal descriptors were used. Results: The predictive potential of developed models has been checked with three random splits into training, passive training, calibration, and validation sets.
The proposed models give satisfactory predictive models for both endpoints examined. Conclusions: The results of the study show the possibility of SMILES-based QSAR in the evaluation of the antibacterial activity of structurally diverse compounds for both endpoints.
Although the developed models give satisfactory predictive models for both endpoints examined, splitting has an apparent influence on the statistical quality of the models.