Predictive Quantitative Structure-activity Relationship Models Research Articles

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C⋯321) are some significant promoters of endpoint increase.

Machine learning models to predict ligand binding affinity for the orexin 1 receptor

The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.

Physicochemical significance of Topological indices: Importance in drug discovery research.

Quantitative Structure-Activity Relationship (QSAR) studies describing the correlations between biological activity as dependent parameters and physicochemical and structural descriptors, including topological indices (TIs) as independent parameters, play an important role in drug discovery research. The emergence of graph theory in exploring the structural attributes of the chemical space has led to the evolution of various TIs, which have made their way into drug discovery. The TIs are easy to compute compared to the empirical parameters, but they lack physiochemical interpretation, which is essential in understanding the mechanism of action. Hence, efforts have been made to review the work on the advances in topological indices, their physicochemical significance, and their role in developing QSAR models. A literature search has been carried out, and the research article providing evidence of the physicochemical significance of the topological parameters as well as some recent studies utilizing these parameters in the development of QSAR models, have been evaluated. In this review, the physicochemical significance of TIs have been described through their correlations between empirical parameters in terms of explainable physicochemical properties, along with their application in the development of predictive QSAR models. Most of these findings suggest a common trend of TIs correlation with MR rather than logP or other parameters; nevertheless, the developed models may be useful in both drug and vaccine development.

Novel and Predictive QSAR Model and Molecular Docking: New Natural Sulfonamides of Potential Concern against SARS-Cov-2

Background: Since the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vac-cines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules. Methods: In this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity Results: The study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was val-idated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Further-more, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation. Conclusion: Our findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase.

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na+/K+-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments. The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities. The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development. The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse

Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals’ toxicity based on large toxicity data sets, but they are limited in insufficient model robustness due to poor data set quality for chemicals with certain structures. To address this issue and improve model robustness, we built a large data set on rat oral acute toxicity for thousands of chemicals, then used ML to filter chemicals favorable for regression models (CFRM). In comparison to chemicals not favorable for regression models (CNRM), CFRM accounted for 67% of chemicals in the original data set, and had a higher structural similarity and a smaller toxicity distribution in 2–4 log10 (mg/kg). The performance of established regression models for CFRM was greatly improved, with root-mean-square deviations (RMSE) in the range of 0.45–0.48 log10 (mg/kg). Classification models were built for CNRM using all chemicals in the original data set, and the area under receiver operating characteristic (AUROC) reached 0.75–0.76. The proposed strategy was successfully applied to a mouse oral acute data set, yielding RMSE and AUROC in the range of 0.36–0.38 log10 (mg/kg) and 0.79, respectively.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R2) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds.

The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

Extensive Study of DFT-Quantum Calculations Based QSAR Modeling of Fused 1,2,4-Triazine Derivatives Revealed Potent CYP1A1 Inhibitors

In a continuing effort for our research group, we identify new active 1,2,4-triazine derivatives as novel cancer preventive agents that target CYP1A1 activity, through the application of quantitative structure–activity relationship (QSAR) theory. For this purpose, novel fused 1,2,4-triazine derivatives, namely, 4-Amino-6-substituted benzyl-3-(2-substituted benzylidenehydrazinyl)-1,2,4-triazin-5(4H)-ones and acetylated 6-substituted benzyl-7-oxo-3-substituted phenyl-2,3-dihydro-7[Formula: see text]-[1,2,4]triazolo[4,3-[Formula: see text]][1,2,4]triazine analogs were utilized for the prediction of the QSAR model. Furthermore, the application of this QSAR equation served successfully as a rational guide for predicting the activities of newly synthesized analogs. The DFT calculation was applied to recognize the physicochemical parameters at B3LYP/6- 311G* level to gain a clear view of global and local molecular reactivity. Besides, the molecular features of the investigated compounds were identified through HOMO, LUMO and molecular electrostatic potential (MEP) which were plotted to determine the charge transfer within the molecules. Promising two novel 1,2,4-triazine compounds were identified with observed significant inhibitions (83% and 67%) and CYP1A1 activity was predicted. The chemical configuration of the two actives showed good alignment of [Formula: see text]-methoxy benzyl aminotriazinone fragments, hydrazino and carbonyl linker in structure–activity relationship and interaction. Molecular modeling analysis supported with MD simulations was undertaken to investigate different binding interactions with the target binding site. These projects offered a good application of a promising QSAR model for prediction of highly active compounds for further lead optimization.

Mechanistic elucidation and prediction of the anticancer activity of 1,3,4-thiadiazol-2-amide derivatives

The development of the potent safer drugs is still urgently needed in the anticancer therapy. To better explore the structural requirements of 1,3,4-thiadiazol-2-amides for their focal adhesion kinase inhibitory activity and their antiproliferative activity towards MCF-7 and B16-F10 cells, six statistically significant and highly predictive quantitative structure-activity relationship models were established, and the corresponding design strategy was also proposed. Our results visualized the main steric and electrostatic profiles in their structures required for the anticancer activity, and showed that the effect of the size, number and the electronegative properties of the substituents in the aromatic rings on these anticancer activities was significant. Finally, the anticancer activity of a series of newly designed compounds was predicted and mutually validated by the established models, and some of which were better than that of the anticancer drug staurosporine.

Suggestion of active 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model

ABSTRACT The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski’s rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

Biological Activities Related to Plant Protection and Environmental Effects of Coumarin Derivatives: QSAR and Molecular Docking Studies.

The aim was to study the inhibitory effects of coumarin derivatives on the plant pathogenic fungi, as well as beneficial bacteria and nematodes. The antifungal assay was performed on four cultures of phytopathogenic fungi by measuring the radial growth of the fungal colonies. Antibacterial activity was determined by the broth microdilution method performed on two beneficial soil organisms. Nematicidal activity was tested on two entomopathogenic nematodes. The quantitative structure-activity relationship (QSAR) model was generated by genetic algorithm, and toxicity was estimated by T.E.S.T. software. The mode of inhibition of enzymes related to the antifungal activity is elucidated by molecular docking. Coumarin derivatives were most effective against Macrophomina phaseolina and Sclerotinia sclerotiorum, but were not harmful against beneficial nematodes and bacteria. A predictive QSAR model was obtained for the activity against M. phaseolina (R2tr = 0.78; R2ext = 0.67; Q2loo = 0.67). A QSAR study showed that multiple electron-withdrawal groups, especially at position C-3, enhanced activities against M. phaseolina, while the hydrophobic benzoyl group at the pyrone ring, and –Br, –OH, –OCH3, at the benzene ring, may increase inhibition of S. sclerotiourum. Tested compounds possibly act inhibitory against plant wall-degrading enzymes, proteinase K. Coumarin derivatives are the potentially active ingredient of environmentally friendly plant-protection products.

The use of minimal topological differences to inspire the design of novel tetrahydroisoquinoline analogues with antimalarial activity

A quantitative structure-activity relationship (QSAR) study was conducted using nineteen previously synthesized, and tested 1-aryl-6-hydroxy-1,2,3,4-tetrahydroisoquinolines with proven in vitro activities against Plasmodium falciparum. In order to computationally design and screen potent antimalarial agents, these compounds with known biological activity ranging from 0.697 to 35.978 μM were geometry optimized at the B3LYP/6-311 + G(d,p) level of theory, using the Gaussian 09W software. To calculate the topological differences, the series of the nineteen compounds was superimposed and a hypermolecule obtained with s¯ = 17 and 20 vertices. Other molecular descriptors were considered in order to build a highly predictive QSAR model. These include the minimal topological differences (MTD), LogP, two dimensional polarity surface area (TDPSA), dipole moment (μ), chemical hardness (η), electrophilicity (ω), potential energy (Ep), electrostatic energy (Eele) and number of rotatable bonds (NRB). By using a training set composed of 15 randomly selected compounds from this series, several QSAR equations were derived. The QSAR equations obtained were then used to attempt to predict the IC50 values of 4 remaining compounds in a test (or validation) set. Ten analogues were proposed by a fragment search of a fragment library containing the pharmacophore model of the active compounds contained in the training set. The most active proposed analogue showed a predicted activity within the lower micromolar range.

Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration.

In ligand-based drug design, quantitative structure–activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC50). Experimental IC50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

The Support vector regression (SVR) was used to investigate quantitative structure–activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

Chemoinformatics Studies on a Series of Imidazoles as Cruzain Inhibitors.

Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure–activity relationships (QSARs) were generated along with the investigation of enzyme–inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r2pred = 0.80) and AutoQSAR (q2 = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r2pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors.

Development of QSAR models for prediction of fish bioconcentration factors using physicochemical properties and molecular descriptors with machine learning algorithms

Bioconcentration factors (BCFs) are indicators of the accumulation of chemical substances in organisms; they play an important role in the environmental risk assessment of various chemical substances. Experiments to obtain BCFs are expensive and time consuming; hence, it is desirable to predictively determine BCF during the early stage of chemical development. In this study, we developed a quantitative structure-activity relationship (QSAR) model using physicochemical properties, environmental fate endpoints, and molecular descriptors. Physicochemical properties and environmental fate endpoints were generated by OPERA, which is a QSAR software. Moreover, we calculated the molecular descriptors using Mordred. A gradient boosting decision tree model was developed as a machine learning model, and multiple linear regression and support vector machine models were developed for comparison. Our developed model showed that the coefficients of determination (R2) of the training and test sets were 0.923 and 0.863, respectively, which are higher than the predictions of the previous model and values calculated by OPERA. The results obtained from the present study suggest that an accurate QSAR model can be developed using the physicochemical properties, environmental fate endpoints, and molecular descriptors calculated from the chemical structure without actually conducting BCF experiments. The model could be one of the choice for the preliminary risk assessment without investing in a large number of BCF experiments during the early development stages of candidate chemicals.

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

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.