Quantitative structure-activity relationship modeling predicts antibacterial efficacy against multidrug-resistant Pseudomonas aeruginosa, suggesting potential for new drug development.
The growing occurrence of multidrug‐resistant Pseudomonas aeruginosa requires the new antibacterial drugs. This research utilizes quantitative structure‐activity relationship (QSAR) modeling to assess and predict the antibacterial efficacy of compounds targeting P. aeruginosa (MTCC 1688). Using advanced computational tools such as PaDEL–Descriptor and QSARINS software, molecular descriptors were created and refined to build strong QSAR models. A systematic technique was utilized for data pre‐processing, involving the elimination of unnecessary descriptors and the creation of training and prediction datasets. The model showed robust internal validation, but its external performance was restricted, emphasizing the necessity for dataset enlargement. The research provides a valuable basis for the future development of drugs aimed at resistant P. aeruginosa strains.
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Gajjar et al. (2025) studied this question.
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