QSAR Research Articles

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations.

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%–98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

In silico models for the screening of human transthyretin disruptors

The use of New Approach Methodologies (NAMs), such as Quantitative Structure-Activity Relationship (QSAR) models, is highly recommended by international regulations to speed up hazard and risk assessment of Endocrine Disruptors, which are known to be linked to a wide spectrum of severe diseases on humans and wildlife. A very sensitive target for these chemicals is the thyroid hormone system, which plays a key role in regulating metabolic and cognitive functions. Several chemicals have been demonstrated to compete with the thyroid hormone thyroxine (T4) for binding to human thyroid hormone distributor protein transthyretin (hTTR). In this work, we generated three new datasets composed by T4-hTTR competing potencies of more than 200 heterogeneous chemicals measured by three different in vitro assays. These datasets were used for the development of new regression QSAR models. The best models were thoroughly validated by internal and external validation procedures. The mechanistic interpretation of the selected molecular descriptors provided information on structural features which are relevant to characterise hTTR binders, such as the presence of hydroxylated and halogenated aromatic rings. PCA analysis was used to rank the studied chemicals according to their increasing T4-hTTR competing potency. Hydroxylated and halogenated bicyclic aromatic compounds are ranked as the strongest hTTR binders. The new QSARs are useful to screen potential Thyroid Hormone System-Disrupting Chemicals (THSDCs), and to support the identification of sustainable alternatives to hazardous chemicals.

New binary mixtures of fungicides against Macrophomina phaseolina: Machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation

Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q2LMO of 0.81, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts.

Exploring Phytochemical Compounds Against Pseudomonas Aeruginosa Using QSAR, Molecular Dynamics, and Free Energy Landscape

AbstractPseudomonas aeruginosa is a versatile opportunistic bacterium that presents a considerable risk in medical environments because of its strong adaptability and resistance to multiple medications. Targeting the LasR quorum sensing system, which plays a crucial role in controlling virulence factors and biofilm formation, is a key intervention point. In this study, in silico molecular docking, machine learning‐based Quantitative Structure‐Activity Relationship (QSAR) techniques along with molecular dynamics simulation were employed to screen phytochemical compounds for their ability to inhibit the LasR QS system, a key regulator of virulence in Pseudomonas aeruginosa. This study screened 1652 phytochemicals using the ML‐based QSAR model to identify 52 phytochemicals that had better activity than the control (N‐{[3,5‐dibromo‐2‐(methoxymethoxy)phenyl]methyl}‐2‐nitrobenzamide). The in silico molecular docking approach that targeted LasR identified compounds 5281647, 57331045, and 5281672 with high binding affinity and hydrogen bonds that were comparable to the control (docking score=−10.3 kcal/mol and hydrogen bonds=4). In the 200 ns post‐molecular dynamics simulation, 5281647 exhibited a stable RMSD of 0.25 nm, which was comparable to the control. The maximum number of hydrogen bonds was exhibited by 57331045, while 5281647 and 5281672 consistently exhibited four hydrogen bonds. Overall, the Principal component analysis (PCA) and Free Energy Landscape (FEL) analyses of the complexes demonstrated that these three compounds were in stable states. In comparison to the control (ΔGTOTAL=−39.58 kcal/mol), the cumulative binding free energy (ΔGTOTAL) for 5281647 and 57331045 was −39.95 kcal/mol and −39.25 kcal/mol, respectively. This further confirms the superior binding affinity of the two compounds. Both 5281647 and 57331045 were identified as potent inhibitors of the LasR transcription factor, which is essential for the quorum sensing of Pseudomonas aeruginosa, in the present investigation. These findings underscore the importance of further exploration and optimization of phytochemicals for combating bacterial infections, offering promising avenues for future drug discovery efforts targeting this resilient pathogen.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling.

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

An integrated In-Silico-In-Chemico-In-Vitro (iSiCiV) Approach to identify biomarkers to predict the skin sensitisation potential of phytochemicals

BackgroundSkin sensitisation is one of the important regulatory endpoints used to determine whether a given test chemical can elicit an allergic response in susceptible individuals. The in-vitro approaches for predicting skin sensitisation potential for chemicals have been well-developed and widely accepted. Continuous efforts are being made to identify novel solutions to overcome the limitations of the existing approaches. PurposeThe herbal preparations used in dermal therapeutics are frequently found to elicit skin sensitisation, requiring caution. The herbal preparations tend to contain more than one phytochemical. The expected active ingredient may not possess skin sensitisation. However, the other phytochemical traces could pose synergistic and potentiating effects towards enhanced/suppressed skin sensitisation. In this regard, isolation, purification, yield, and in-vitro / in-vivo evaluation of skin sensitisation for all the traces would be time, cost, and animal-consuming. Hence, the regulatory agencies appreciate using computational tools to substantiate in-vitro methods. The regulators have also endorsed using in-silico approaches like quantitative structure-activity relationships (QSAR) and read-across. These approaches are limited to structure-based predictions and in combination with system-level approaches, which comprehensively facilitate a better understanding of complex biological systems. Study designIn the present study, 603 phytochemicals were screened in-silico using OECD QSAR Toolbox. The limitations of the QSAR Toolbox in predicting skin sensitisers were identified. MethodThe systems biology-based approach to complement the QSAR-based prediction was developed, and 41 biomarkers were identified as a determinant of skin sensitisation. The molecules attributed to these biomarkers were predicted as skin sensitisers. ResultsAs a result, 31 phytochemicals were predicted as skin sensitisers. Acrolein, eugenol, formaldehyde, and glycerol were selected for experimental validation of the in-silico predictions using direct peptide reactivity assay (DPRA), KeratinoSens™, and h-CLAT assays representing each key event in skin sensitisation. Further, identified biomarkers were validated in THP-1 and KeratinoSens cell lines using qRT-PCR. Among all genes, IL-8 and CCL4 were upregulated by 6.4 and 5.6 folds, respectively, in THP-1, while AKR1C2 and AKR1C3 were upregulated by 7.8 and 11.1 folds, respectively, in KeratinoSens. IL-8 level was analysed by ELISA and found to be in line with qRT-PCR results. ConclusionThe study proposes a possible strategy based on the “iSiCiV” approach to enhance the prediction of the dermal sensitisation potential of a test compound. The approach offers a robust platform and highlights an opportunity to shift the paradigm of skin sensitisation assay with a better understanding of toxicology mechanisms.

Bicubic regression analysis a novel approach for estimation of physicochemical properties of skin cancer drugs through degree based entropy

Skin cancer poses a significant risk to the healthcare system worldwide and is projected to increase substantially over the next two decades, particularly if not detected in its early stages. The primary aim of this study is to construct a quantitative structure-property relationship (QSPR) by correlating calculated entropies with topological indices and specific physical-chemical properties of pharmaceuticals, in order to enhance their usefulness. The bicubic regression model is constructed through degree-based topological entropies to perform the QSPR analysis for the prediction of physiochemical properties like polar surface area, complexity, molar refractivity, boiling point, and polarity of skin cancer drugs. It is also examined that degree-based entropies provide best-fit models for skin cancer physiochemical properties. We inspected five physio-chemical properties of these anticancer drugs and found this methodology suitable for predicting best-fit approximations regarding R2 value. It is expected that this approach will be very favourable to examining problems related to mathematical chemistry.

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R2 = 0.98142, Q2LOO = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model's reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties.

Application of Machine Learning Methods in Predicting Lysine-specific Histone Demethylase 1 (LSD1) Inhibitors

Background: Lysine-specific histone demethylase 1 (LSD1) is a well-known anti-cancer target for drug discovery. Novel reversible inhibitors of LSD1 are desirable to be developed. Objective: This study aimed to build reliable predictive models to evaluate the antineoplastic efficacy of compounds and reveal the structural foundation underlying the inhibitory activity of LSD1. Methods: Multiple artificial intelligence algorithms were utilized in the development of quantitative structure-activity relationship (QSAR) models. A dataset comprising 915 compounds with welldefined IC50 values against LSD1 was assembled for analysis. The structures of these compounds were described by different descriptor packages. Principal component analysis (PCA) was performed to explore the chemical space distribution of each dataset. Y-randomization test and applicability domain (AD) analysis were deployed to validate the reliability of models. Results: For regression models, a consensus model was constructed by integrating the predictions of four top-performing individual models (SVM_ECFP4, RFR_PyDescriptor, RFR_RDKIT, and TRANSNNI), resulting in enhanced predictive performance as compared to the individual models. The consensus model achieved a determination coefficient (r2, for the test set) value of 0.82. For classification models, the consensus model was derived from the amalgamation of three individual models (ASNN_RDKIT, ASNN_ECFP4, and RFR_ECFP4), and the overall prediction accuracy of the model was 0.96 for external validation. Conclusion: The reliable models could be used to identify highly active LSD1 inhibitors for the purpose of efficient virtual screening. In addition, the important molecular properties and structural fragments derived from this work can provide guidance for the structural optimization of novel LSD1 inhibitors.

Genotoxicity of beauvericin

Abstract The European Commission (EC) asked EFSA to assess the genotoxicity of beauvericin (BEA). Relevant information, including that which has become available since the 2014 Scientific Opinion on the risks to human and animal health related to the presence of BEA and enniatins in food and feed, was reviewed. In the previous Opinion the Panel concluded that in vitro genotoxicity data were equivocal and there were no in vivo genotoxicity data available. New in vitro studies in mammalian cell lines provided no convincing evidence for induction of chromosomal damage by BEA as measured by micronucleus and chromosome aberration tests or an increase of DNA strand breaks as assessed by the Comet assay. In these studies, no concentration‐dependent effects or potential for interference from associated cytotoxicity were observed. In addition, DNA double‐strand breaks as measured by γ‐H2AX analysis were only observed following exposure to highly cytotoxic BEA concentrations. In vivo studies (Comet and Pig‐a assays, micronucleus test) with BEA were negative. In vitro gene expression studies showed no indication of a DNA damage response and (quantitative) structure activity relationship analysis was also not indicative of genotoxic potential. Some effects of BEA might play an indirect role in the formation of DNA strand breaks. These include increased reactive oxygen species, induction of cell cycle arrest and apoptosis, associated with interference in mitochondrial function and cell signalling. There was no compelling evidence of inflammatory and immunosuppressive effects. Taken together, the available data indicate that BEA is devoid of genotoxic potential.

Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS

In this study, we introduce openCOSMO-RS 24a, an improved version of the open-source COSMO-RS model parameterized using quantum chemical calculations from ORCA 6.0, leveraging a comprehensive dataset that includes solvation free energies, partition coefficients, and infinite dilution activity coefficients for various solutes and solvents mainly at 25 °C. This is the first version of the model also capable of predicting solvation free energies based on ORCA calculations. Additionally, we develop a Quantitative Structure-Property Relationships model to predict molar volumes of the solvents, an essential requirement for predicting solvation free energies and partition coefficients from structure alone. Our results show that openCOSMO-RS 24a achieves an average absolute deviation of 0.45 kcal mol1 for solvation free energies, 0.76 for the logarithm of the partition coefficients, and 0.51 for the logarithm of infinite dilution activity coefficients, demonstrating improvements over the previous openCOSMO-RS 22 parameterization and comparable results to COSMOtherm 24 BP-TZVP. The user interface was extended to be able use it as solvation model directly from within ORCA 6.0 or from the command line to provide researchers with a robust tool for applications in chemical and materials science.

A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Those undergoing pregnancy are often excluded from clinical drug trials due to the risk that participation would pose. However, they often require pharmaceuticals to manage health conditions that, if gone untreated, could harm themselves or the fetus. This can mean that such individuals take one or more pharmaceuticals during pregnancy, many of which have unknown reproductive effects. Machine learning models have been used to successfully predict a number of reproductive toxicological outcomes for pharmaceuticals, including transplacental transfer, US Food and Drug Administration safety rating, and drug interactions. Models use quantitative chemical and structural features of active compounds to make predictions concerning the outcome of interest using computational algorithms. Results from these models can be a potential source of valuable information for pregnant people and their medical providers when making decisions regarding therapeutic drug use. This review summarizes current machine learning applications to make predictions about risk and toxicity of medication use during pregnancy. Our review of the recent literature revealed that machine learning quantitative structure-activity relationship models can be used successfully to predict the transplacental transfer and the US Food and Drug Administration pregnancy safety category of pharmaceuticals; such models have also been employed to predict drug interactions, though not specifically during pregnancy. This latter topic is a potential area for future research. In this review, no single algorithm or descriptor-calculation software emerged as the most widely used, and their performances depend on a variety of factors, including outcome of interest and combination of such algorithms and software.

Using the Super-learner to Predict the Chemical Acute Toxicity on Rats

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9,843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

In Silico Prediction of EGFR Inhibitors from Thiophene Derivatives

Abstract: Cancer is one of the biggest global health problems and is the second leading cause of death worldwide. Cancer also causes great damage to economy. Unfortunately, there is still no effective treatment method against this disease today, and the mortality rates in certain types are still very high. Medical research can now be done faster and safer with the aid of in silico studies. These studies save time for researchers and accelerate new drug discoveries. In our study, thiophene derivatives with important efficacy in cancer treatment were focused on and the affinity of the small molecule structures determined as candidates to the Epidermal Growth Factor Receptor (EGFR), known to be the key receptor in cancer, was examined. First, molecular docking studies were performed, and then long-term molecular dynamics (MD) simulations were carried out. Finally, anti-cancer activity predictions based on Quantitative Structure-Activity Relationship (QSAR) were performed. Co-crystallized ligand Erlotinib, taken from the Protein Data Bank (PDB), was used as a positive control and compared with candidate drugs using the same procedures. In light of the analysis of virtual screening, MD, MM/GBSA, and QSAR predictions, the top three molecules and their MM/GBSA scores were identified as follows: OSI 930 (-65.81 kcal/mol), Neltenexine (-49.53 kcal/mol), and Tenonitrozole (-41.95 kcal/mol). As a result, in this study, candidate molecules that inhibit EGFR and have the highest potential as anti-cancer drugs among thiophene-derived compounds were determined and detailed in silico analyzes were performed. This study holds importance as it may guide future anti-cancer drug discovery studies.

QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Photodegradation of polychlorinated biphenyls (PCBs) on suspended particles from the Yellow River under sunlight irradiation: QSAR model and mechanism analysis

Polychlorinated biphenyls (PCBs), as a class of hydrophobic organic pollutants, are widely found in river sediments and suspended particles, and the environmental fate of different PCBs can be better understood by investigating their photochemical transformation process. In this study, the quantitative structure-activity relationship (QSAR) model between the photodegradation rate constants of 17 PCBs adsorbed on Yellow River suspended particles in water (as a typical heterogeneous photodegradation system) and the physicochemical parameters of PCBs was constructed by SPSS and machine learning. The model showed that the more hydrophobicity of the molecule, the more positive charge carried by the aromatic C atoms, and the presence of chlorine atoms adjacent to the carbon bridge could all enhance the photochemical activity of PCBs. From the combined analysis of rate constants, quenching experiments and theoretical calculations, it was revealed for the first time that in natural suspended particle containing organic matter, the higher concentration of •O2- and 1O2 in the hydrophobic zone contributed more to the more hydrophobic PCBs, while •OH in the hydrophilic zone played a major role in the degradation of the less hydrophobic PCBs. Findings of this study would deepen the understanding of the degradation mechanism of hydrophobic pollutants by active species in complex environments.

Computing Zagreb Connection Indices for the Cartesian Product of Path and Cycle Graphs

Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and Quantitative Structure-Activity Relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a function. Among the Zagreb connection indices have been extensively researched. This study delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first , second , and third . Furthermore, comprehensive results of the modified first , modified second , and modified third are presented, along with the first multiplicative ZCI second multiplicative ZCI (SMZCI), and third multiplicative ZCI Moreover, modified first multiplicative ZCI ), modified second multiplicative ZCI and modified third multiplicative ZCI ( are also calculated. To provide precision, both the graphical and numerical analyses of the computed findings are aligned for the two Cartesian products. The foundation for mathematically modeling complex networks and chemical structures lies in graph theory. Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and quantitative structure-activity relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a topological index function. Among these the Zagreb connection indices are extensively researched TIs. This article delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first, Second and third. Furthermore, we present the comprehensive results of the modified , modified and modified , and also present the first multiplicative Zagreb connection index , and , along with their modified counterparts like modified first multiplicative Zagreb connection index , and . These analysis encompass two distinct types of graphs: cycle graphs and path graphs. To provide further precision, we align both the graphical and numerical analysis of our computed findings for these two Cartesian products.

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.