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An ANN-Based QSAR Model to Predict Anti-Staphylococcus aureus Activity of Oxadiazoles

Kamrani SME and Hadizadeh F*

Background: Antibiotic-resistant bacteria are likely to be one of the most critical problems in the future; hence, more effort is needed to design and develop new types of antimicrobial agents. Quantitative Structure-Activity Relationship (QSAR) is a procedure which helps other researchers to design better chemical agents with more potent biological activity. QSAR can be defined as a quantitative relationship between the chemical structure and biological activity.

Results: In this study, a previously-synthesized oxadiazole library was used to build a QSAR model based on the Group Method of Data Handling (GMDH) method and Partial Least Squares (PLS) regression. Owing to their high correlation coefficients (R2) for test and training data, both methods are sufficiently reliable. In this study, the active compounds of the library were used as a template to design new chemical compounds predicted to have a great anti-Staphylococcus aureus (S. aureus) activity according to PLS, GMDH, and docking methods. GMDH and PLS are highly flexible such that they can include other information like absorption, distribution, metabolism, and excretion (ADMT) and toxicity.

Conclusion: The selected methods can be used to handle huge amounts of data from a large library of chemical compounds and help research and development (R&D) process. Additionally, the designed model and the proposed compounds can help other researchers to find the best anti- Staphylococcus aureus chemical compounds.