Abstract
Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.
Highlights
Received: 25 September 2020Accepted: 24 December 2020Published: 27 December 2020Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.license.Quantitative structure–activity relationship (QSAR) studies identify key descriptors and properties of structurally related molecules that are correlated to biological activities based on the concept that similar compounds have similar activities [1,2]
We report a convenient method to develop quantitative structure activity relationship (QSAR) models based on topological descriptors and density functional theory derived molecular orbital properties that is capable of predicting antifungal and antibiotic activity against Aspergillus species, Penicillium species, Gram-positive Staphylococcus aureus and Bacillus subtilis, and Gram-negative Escherichia coli and Pseudomonas aeruginosa [10]
We demonstrate the application of QSAR methodology to a series of triazolothiadiazine derivatives to gain insight into the contributions of electronic and chemical properties to seven different antifungal and antibiotic activities
Summary
Quantitative structure–activity relationship (QSAR) studies identify key descriptors and properties of structurally related molecules that are correlated to biological activities based on the concept that similar compounds have similar activities [1,2]. This information can be used to develop predictive models for the design and evaluation of new antimicrobials or to economically screen libraries of compounds for antimicrobial properties in silico [3,4]. The resistance of pathogenic fungi and bacteria to popular antifungals and other antimicrobials negatively impacts public health throughout the world and spurred recent calls for the development of new antimicrobials [5,6]. Phenolic compounds, including components of essential oils, exhibit a variety of antimicrobial properties [11,12]
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