Predictive Quantitative Structure–Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds

• Main Authors: Michael Appell, David L. Compton, Kervin O. Evans
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Methods and Protocols
• Subjects: antifungal; antimicrobial; food safety; machine learning; mycotoxin
• Online Access: https://www.mdpi.com/2409-9279/4/1/2

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 <i>Aspergillus niger</i>, <i>Aspergillus flavus</i> and <i>Penicillium</i> sp., as well as the bacteria <i>Staphylococcus aureus</i>, <i>Bacillus subtilis</i>, <i>Escherichia coli</i>, and <i>Pseudomonas aeruginosa</i>. 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 <i>R</i>² 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.