Quantitative Structure-activity Relationship Approach Research Articles

Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D–2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D–2D descriptors. Then, the combined pool of RASAR and the identified 0D–2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.

Design of acyl salicylic acid derivates as COX-1 inhibitors using QSAR approach, molecular docking and QSPR analysis

Background: Acetylsalicylic acid (aspirin), widely used as an antiplatelet agent, is more likely to inhibit COX-1. Along with discovering the cardioprotective role of COX-1 in controlling platelet aggregation, it is important to develop a selective COX-1 inhibitor. Objective: This study aims to design acyl salicylic acid derivatives intended as COX-1 inhibitors. Method: Fourteen derivatives (AcS1-14) were subjected to a quantitative structure-activity relationship (QSAR) study, and 31 QSAR models were obtained using multiple linear regression (MLR) analysis. Molecular docking was performed on COX-1 (PDB. 1PTH) using the Molecular Orbital Environment (MOE) program ver2022.02, and QSPR analysis was conducted to ascertain the contribution of physicochemical descriptors to the free energy score (S) of ligand-receptor complexes. Results: The QSAR-Hansch model predicted hydrophobicity (LogP) and molecular energy (Etotal) and contributed to pain inhibitory action. All derivatives displayed higher in silico affinity than aspirin (S= -4.33±0.00 kcal/mol), and compound AcS7 afforded the highest (S= -5.32 kcal/mol). In QSPR, Etotal also revealed a positive contribution to the affinity. AcS1, AcS2, AcS5, AcS7, and AcS8 expressed higher drug-like properties than aspirin. Conclusion: Derivatives with optimum hydrophobicity and high energy would generate potent COX-1 inhibition. The five selected compounds were recommended to be developed as drug candidates for COX-1 inhibitors.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to treat Metabolic Syndrome.

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism. To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome. In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor. The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 µM and 0.0027 µM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed. The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis.

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

Rational Design of a Potential New Nematicide Targeting Chitin Deacetylase.

In a previously published study, the authors devised a molecular topology QSAR (quantitative structure-activity relationship) approach to detect novel fungicides acting as inhibitors of chitin deacetylase (CDA). Several of the chosen compounds exhibited noteworthy activity. Due to the close relationship between chitin-related proteins present in fungi and other chitin-containing plant-parasitic species, the authors decided to test these molecules against nematodes, based on their negative impact on agriculture. From an overall of 20 fungal CDA inhibitors, six showed to be active against Caenorhabditis elegans. These experimental results made it possible to develop two new molecular topology-based QSAR algorithms for the rational design of potential nematicides with CDA inhibitor activity for crop protection. Linear discriminant analysis was employed to create the two algorithms, one for identifying the chemo-mathematical pattern of commercial nematicides and the other for identifying nematicides with activity on CDA. After creating and validating the QSAR models, the authors screened several natural and synthetic compound databases, searching for alternatives to current nematicides. Finally one compound, the N2-(dimethylsulfamoyl)-N-{2-[(2-methyl-2-propanyl)sulfanyl]ethyl}-N2-phenylglycinamide or nematode chitin deacetylase inhibitor, was selected as the best candidate and was further investigated both in silico, through molecular docking and molecular dynamic simulations, and in vitro, through specific experimental assays. The molecule shows favorable binding behavior on the catalytic pocket of C. elegans CDA and the experimental assays confirm potential nematicide activity.

Machine Learning-Based Enhanced Drug Delivery System and Its Applications – A Systematic Review

Machine Learning (ML) methods offer advanced algorithms and tools to enhance drug development. Quantitative Structure-Activity Relationship (QSAR) approaches have successfully predicted several physicochemical aspects of pharmaceuticals, including toxicity, intake, drug-drug interactions, carcinogenesis, and dispersion. ML, a branch of artificial intelligence, has demonstrated significant promise in drug development. The methods presented in this study can model non-linear databases and handle big data that is becoming more extensive and intricate. Diverse ML methodologies are currently employed for making forecasts of drug targets, modeling the framework of drug targets, forecasting binding sites, conducting ligand-based similarity searches, designing novel ligands with specific properties, creating scoring algorithms for molecular docking, constructing QSAR models for predicting biological reactions, and forecasting the pharmacokinetic and pharmacodynamic characteristics of ligands. The findings of this study illustrate the widespread utilization of ML techniques in drug discovery, suggesting a favorable outlook for these advances. These findings have the potential to facilitate further exploration of ML in connection with drug discovery and growth by researchers, learners, and pharmaceutical companies.

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. method: CoMFA, CoMSIA combining HQSAR methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. result: Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA) combining hologram quantitative structure-activity relationship (HQSAR) methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Result: The obtained models can be used to predict the activities of new flavonoids and identify the key structural features affecting the NO inhibitory activities. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors. other: none

Efficient removal of organic pollutants by activation of peroxydisulfate with the magnetic CoFe2O4/carbon nanotube composite.

A magnetic composite of CoFe2O4 and carbon nanotube (CNT) was prepared using the solvothermal approach and then employed for the activation of peroxydisulfate (PDS) to degrade reactive black 5 (RB5) and other organic pollutants. Characterization results of the composite catalyst revealed the successful loading of spherical CoFe2O4 particles on CNTs, possessing abundant porosity as well as magnetic separation capability. Under the degradation conditions of 0.2g/L CoFe2O4-CNT dosage and 4mM PDS dosage, the removal efficiencies of 10mg/L RB5 and other pollutants were in the range of 94.5 to ~ 100%. The effects of pH, co-existing ions/humic acid, and water matrices as well as the reusability of the catalyst were also investigated in detail. Furthermore, the degradation mechanism and pathway were proposed based on quenching experiments, LC-MS analysis, and density functional theory (DFT) calculations, and the toxicity of the degradation products was evaluated in the quantitative structure-activity relationship approach.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Effect of the Electronic Structure of Para-Substituted Benzaldehyde Benzohydrazone on Its Antimicrobial Activity: A DFT Analysis

Bacillus subtilis is a bacterium that has demonstrated its efficacy across various domains, including industry, agriculture, and commerce, owing to its protective, inhibitory, and biological mechanisms against specific microbes. However, at high concentrations, it can lead to food poisoning and severe infections, resulting in symptoms such as diarrhea and vomiting. Bacterial spores produced by Bacillus subtilis can induce conditions like gas gangrene and tetanus. In this context, benzohydrazones are recognized for their antimicrobial activity, particularly against Bacillus subtilis. This study aims to elucidate the relationship between the electronic structure of para-substituted benzaldehyde benzohydrazone derivatives and their antimicrobial activity. This leads to the proposal of a 2D pharmacophore for predicting the antibacterial activity of these derivatives. The quantitative structure-activity relationship (QSAR) approach employed is the KPG method. The electronic structures were optimized using the density functional theory (DFT) method with the B3LYP functional and the 6-31G (d,p) basis set. Charge and local molecular orbitals were considered in the optimization process. The resulting prediction equation (R=98.95%, R²=97.91%, Adjusted R²=96.76%, F(5,9)=84.52) derived from multiple linear regression provides the basis for the proposed 2D pharmacophore. This equation shows that antimicrobial activity of benzohydrazone derivative is on charge and orbital controlled. This pharmacophore holds potential utility in designing new molecular structures with enhanced activity against Bacillus subtilis.

Mechanistic insights into the selectivity of bicyclophosphorothionate antagonists for housefly versus rat GABA receptors.

γ-Aminobutyric acid (GABA) receptors (GABARs) are validated targets of insecticides. Bicyclophosphorothionates are a group of insecticidal compounds that act as noncompetitive antagonists of GABARs. We previously reported that the analogs exhibit various degrees of selectivity for housefly versus rat GABARs, depending on substitutions at the 3- and 4-positions. We here sought to elucidate the unsolved mechanisms of the receptor selectivity using quantitative structure-activity relationship (QSAR), molecular docking, and molecular dynamics approaches. Three-dimensional (3D)-QSAR studies using Topomer comparative molecular field analysis quantitatively demonstrated how the introduction of a small alkyl group at the 3-position of bicyclophosphorothionates contributes to the housefly versus rat GABAR selectivity. To investigate the molecular mechanisms of the selective action, bicyclophosphorothionates were docked into housefly Resistance to dieldrin (RDL) GABAR and rat α1β2γ2 GABAR homology models built using the published 3D-structures of human GABARs as templates. The results of molecular docking and molecular dynamics simulations revealed that the 2'Ala and 6'Thr residues of the RDL subunit within the channel are the key amino acids for binding to the housefly GABARs, whereas the 2'Ser residue of γ2 subunit plays a crucial role in binding to rat GABARs. We revealed the molecular mechanisms underlying the selective antagonistic action of bicyclophosphorothionates on housefly versus rat GABARs. The information presented should help design and develop novel, safe GABAR-targeting insecticides. © 2023 Society of Chemical Industry.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.

A generative adversarial network model alternative to animal studies for clinical pathology assessment

Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use.

Hydroxyl radical and UV-induced reactions of bisphenol analogues in water: Kinetics, transformation products and estrogenic activity estimation

There is currently a concern about the endocrine-disrupting capacity of many bisphenol A substitutes, such as BPAF, BPAP, BPB, BPC, BPC-Cl, BPE, BPF, BPS and BPZ in natural waters. However, fundamental data (i.e., kinetics and mechanisms) about the performance of advanced oxidation processes and UV radiation for water decontamination are scarce. In this study, the removal of bisphenol A substitutes was evaluated by UV/H2O2 and UV treatments under neutral pH conditions. Reactivity of hydroxyl radical (·OH) with bisphenol analogues was studied by competition kinetics and their quantum yield was determined at 254 nm. Results revealed similar values of the second-order rate constants of ·OH with all bisphenols (5.89–14.1 × 109 M−1 s−1), as well as comparable values of the quantum yields (4.8–28.7 × 10−3 mol E−1), except for BPC-Cl. This compound showed a remarkably high quantum yield (4.7 × 10−1 mol E−1), which resulted in a removal higher than 60 % at typical UV disinfection doses (ca. 40 mJ cm−2). The transformation products formed by ·OH and UV-induced reactions were also assessed. Catechol and ortho-quinone derivatives were suggested as the main intermediates from the reaction of bisphenols with ·OH. Excluding BPC-Cl, the resulting photolysis products of bisphenols coincided with those from the ·OH reaction. A distinguished mechanism was proposed for the formation of the photolysis products of BPC-Cl, based on the favoured cleavage of the C–Cl bonds under UV irradiation. Phenanthrene-3,6-diol was suggested as main initial photolysis byproduct of BPC-Cl. Estrogenicity of bisphenols and detected intermediates was predicted using a Quantitative Structure-Activity Relationship (QSAR) approach. Certain byproducts produced during bisphenols reaction with ·OH, such as catechol derivatives, may exhibit estrogenic activity, as they were predicted as very strong binders. Similarly, all photolysis intermediates of BPC-Cl were predicted as very strong binders as well, suggesting that estrogenicity could persist after the treatment.

Postmortem redistribution of drugs: a literature review.

Postmortem drug analysis is crucial in identifying the potential cause and manner of death. However, it is threatened by a significant phenomenon called postmortem redistribution (PMR), which refers to the alterations in drug levels occurring after death. This review aims to describe the PMR phenomenon, the mechanisms involved in the PMR of drugs, the various methods used to predict it, and various artifacts of postmortem drug concentrations. Several mechanisms, including passive diffusion from solid organs that act as drug reservoirs to the surrounding tissues, cadaveric changes after death (e.g., cell death, blood coagulation, hypostasis, and movements), and the putrefactive process, can result in artifacts of postmortem drug concentrations. The drug's chemical and pharmacokinetic properties (such as acidic/basic properties, lipophilicity, protein binding, high volume of distribution, and residual metabolic activity) are additional factors. Several markers, including cardiac blood-to-peripheral blood ratio (C/P), liver-to-peripheral blood ratio (L/P), amino acid markers such as methionine, quantitative structure-activity relationship (QSAR) approach, and F factor, have been proposed for interpreting the liability of drugs to PMR. Several artifacts may affect the reliability of postmortem drug analysis. Peripheral blood is preferred for postmortem drug sample collection. Numerous laboratories evaluate the redistribution potential of drugs after death using the C/P concentration ratio. Nevertheless, the L/P concentration ratio is proposed to be a more reliable marker for PMR determination.

P22-34: An in vitro Quantitative Structure-Activity Relationship (QSAR) approach in the risk assessment of PFAS using HepG2 and THP-1 cells

P22-34: An in vitro Quantitative Structure-Activity Relationship (QSAR) approach in the risk assessment of PFAS using HepG2 and THP-1 cells

The importance of good practices and false hits for QSAR-driven virtual screening real application: a SARS-CoV-2 main protease (Mpro) case study

Computer-Aided Drug Design (CADD) approaches, such as those employing quantitative structure-activity relationship (QSAR) methods, are known for their ability to uncover novel data from large databases. These approaches can help alleviate the lack of biological and chemical data, but some predictions do not generate sufficient positive information to be useful for biological screenings. QSAR models are often employed to explain biological data of chemicals and to design new chemicals based on their predictions. In this review, we discuss the importance of data set size with a focus on false hits for QSAR approaches. We assess the challenges and reliability of an initial in silico strategy for the virtual screening of bioactive molecules. Lastly, we present a case study reporting a combination approach of hologram-based quantitative structure-activity relationship (HQSAR) models and random forest-based QSAR (RF-QSAR), based on the 3D structures of 25 synthetic SARS-CoV-2 Mpro inhibitors, to virtually screen new compounds for potential inhibitors of enzyme activity. In this study, optimal models were selected and employed to predict Mpro inhibitors from the database Brazilian Compound Library (BraCoLi). Twenty-four compounds were then assessed against SARS-CoV-2 Mpro at 10 µM. At the time of this study (March 2021), the availability of varied and different Mpro inhibitors that were reported definitely affected the reliability of our work. Since no hits were obtained, the data set size, parameters employed, external validations, as well as the applicability domain (AD) could be considered regarding false hits data contribution, aiming to enhance the design and discovery of new bioactive molecules.

Prospects of Computer-Aided Molecular Design of Coumarins as Ecotoxicologically Safe Plant Protection Agents

Coumarins are secondary plant metabolites widely distributed in higher plants, bacteria, fungi, and sponges. This great structural diversity of these natural compounds and their synthesized derivatives enables their wide range of pharmacological activities, such as antioxidant; antibacterial; antifungal; anti-human immunodeficiency infection; anti-tubercular; and anti-cancer activities, which were very well reviewed previously. There are also many reports about their effectiveness against plant pathogenic pests (phytopathogenic fungi, bacteria, nematodes, and insects). These secondary metabolites protect environmental enemies and competing plants. However, there is still limited literature on coumarins’ practical applications in agriculture, as well as their effects on beneficial populations of soil organisms. This review summarizes recent knowledge about the effects of natural and synthesized coumarins on phytopathogens and beneficial populations of soil organisms. A separate section is dedicated to poorly reviewed computer-aided molecular design (CAMD) methods of agrochemicals. It also reviewed CAMD techniques to develop low-toxicity and environmentally safe pesticides. Despite the many positive effects of coumarins related to plant protection, they do possess properties harmful to the environment and health. These properties are described in the last section. Prediction of coumarin hazardous properties using a quantitative structure-activity relationship (QSAR) approach was also reviewed.

Application of QSAR Approach to Assess the Effects of Organic Pollutants on Bacterial Virulence Factors.

The release of a wide variety of persistent chemical contaminants into wastewater has become a growing concern due to their potential health and environmental risks. While the toxic effects of these pollutants on aquatic organisms have been extensively studied, their impact on microbial pathogens and their virulence mechanisms remains largely unexplored. This research paper focuses on the identification and prioritization of chemical pollutants that increase bacterial pathogenicity, which is a public health concern. In order to predict how chemical compounds, such as pesticides and pharmaceuticals, would affect the virulence mechanisms of three bacterial strains (Escherichia coli K12, Pseudomonas aeruginosa H103, and Salmonella enterica serovar. Typhimurium), this study has developed quantitative structure-activity relationship (QSAR) models. The use of analysis of variance (ANOVA) functions assists in developing QSAR models based on the chemical structure of the compounds, to predict their effect on the growth and swarming behavior of the bacterial strains. The results showed an uncertainty in the created model, and that increases in virulence factors, including growth and motility of bacteria, after exposure to the studied compounds are possible to be predicted. These results could be more accurate if the interactions between groups of functions are included. For that, to make an accurate and universal model, it is essential to incorporate a larger number of compounds of similar and different structures.