QSAR Research Articles

Design of Novel Acat Inhibitors as Potent Anti-Hyperlipidemic Agents Using Chemometric Approaches

Aim: A 2D QSAR study of acyl-coenzyme A (CoA): cholesterol acyltransferase (ACAT) inhibitors revealed that electronic, topological, and steric properties are important structural features required for activity against ACAT. Background: In order to interpret the evidence encrypted by the molecular structure of the compounds, a standard physicochemical descriptors-centered, and Quantitative Structure-Activity Relationship (QSAR) approach was implemented on a data set of Indoline derivatives were reported to be acyl-coenzyme A (CoA): cholesterol acyltransferase ACAT inhibitors. Objective: The ACAT enzyme plays an important role in the absorption of dietary cholesterol. Therefore, the inhibition of ACAT is a key strategy or primary objective for the treatment of hypercholesterolemia and atherosclerosis. Method: Chemo metric models were designed by inserting a battery of statistical techniques in the current study that demonstrate the linear approaches of analysis, including multiple linear regression (MLR), partial least square PLS, and non-linear methods such as artificial neural networks (ANN). Result: The activity contributions of these molecules were analyzed through regression equation, and the best QSAR model was created with an excellent correlative and predictive ability. Significant statistical values S = 0.35, F = 60.30, r = 0.92, r² = 0.85, r² (CV) = 0.82 of the designed models were obtained using stepwise MLR and a comparable PLS and FFNN model with r² (CV) = 0.82, 0.88 and 0.86 respectively and the relevant descriptors like inertia moment 1 size, Kier Chiv4 (cluster) index, Kier Chiv6(ring) index offered important information regarding this model. Conclusion: The model reveals that inertia moment 1 size, Kier Chiv4 (cluster) index, and Kier Chiv6 (ring) index are prerequisite descriptors to determine other promising ACAT antagonists with high and liable potency against the target. Therefore, these characteristics may be used efficiently for the design and evaluation of active compounds as new ACAT inhibitors thanks to their utilization.

Topological Invariants for the Chemical Structures Used in Treatment of COVID-19

A topological index is a quantity expressed as a number that help us to catch symmetry of chemical compounds. With the help of quantitative structure property relationship (QSPR), we can guess physical and chemical properties of several chemical compounds. Here, we will compute Shingali & Kanabour, Gourava and hype Gourava indices for the chemical compounds, used in treatment of COVID-19, namely Remdesivir, Chloroquine, Hydroxychloroquine Theaflavin.

Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus)

A quantitative structure–activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all considered carefully, giving full consideration to the OECD (Organization for Economic Co-operation and Development) principles for QSAR acceptability in regulation during the model construction and assessment process. Nine variables were selected through the forward stepwise regression method and used as inputs to construct both linear and nonlinear models. The obtained models were validated internally and externally. Generally, machine learning-based methods, namely support vector machine (SVM), random forest (RF), and projection pursuit regression (PPR), perform better than the multiple linear regression (MLR) model. The statistical results (R2 = 0.682–0.933, Q2LOO = 0.604–0.659, Q2F1 = 0.740–0.796, CCC = 0.861–0.882) of the developed models show that they are robust, reliable, reproducible, accurate and predictive. Comparatively, the RF model performs best, giving predictive correlation coefficient Q2 of 0.814, root mean squared error (RMSE) of 0.658 and mean absolute error (MAE) of 0.534 for the test set, respectively. The RF model (as well as SVM and PPR models) was visualized and explained by using the SHapley Additive explanation (SHAP) analysis to enhance its transparency and credibility. In addition, the applicability domain (AD) range of the RF model was characterized by the Williams plot and the tree manifold approximation and projection (TMAP) technology was utilized to illustrate similarity and diversity of the entire data space, to assist in the analysis of the outliers. Activity cliff detection was investigated by using Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. It was found that none of the pesticides was identified as an activity cliff in the training set or a potential prediction cliff in the test set. Therefore, the RF model fulfills each OECD principle in regulation for QSAR models. The research in this work will aid in the in silico QSAR prediction of the acute toxicity to Sheepshead minnow (Cyprinodon variegatus) for untested and new toxic pesticides and can also be extended to other studies.

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling

The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-molecule inhibitors targeting the hIL1R1/IL1β interaction, employing a multi-task transfer learning approach for quantitative structure-activity relationship (QSAR) modelling. A comprehensive bioactivity dataset from functionally related proteins was utilised to build a robust ensemble machine learning model for predicting IC50 values against the target protein. Despite the availability of antibody-based therapies, the absence of orally available small-molecule inhibitors necessitates their development. By combining model predictions with docking and simulation approaches, the interleukin-1 receptor inhibitor (IRI-1) emerged as a lead compound. It potently inhibited human IL1-R1 with micromolar activity in THP-1 and Saos-2 cells and demonstrated good biocompatibility. Western blot analysis revealed that IRI-1 inhibits IL-1β-mediated phosphorylation of IL1-R1, JNK, IRAK-4, and ERK in THP-1 cells. Furthermore, molecular dynamics simulations confirmed the structural stability of the protein-ligand complexes. This study highlights the effectiveness of multi-task transfer learning approaches for building robust QSAR models against novel proteins or those with limited bioactivity data, such as hIL-1β/IL-1R1 protein.

Elucidation of molecular mechanisms involved in tadpole toxicity employing QSTR and q-RASAR approach

Tadpoles, as early developmental stages of frogs, are vital indicators of toxicity and environmental health in ecosystems exposed to harmful organic compounds from industrial and runoff sources. Evaluating each compound individually is challenging, necessitating the use of in-silico methods like Quantitative Structure Toxicity Relationship (QSTR) and Quantitative Read-Across Structure Activity Relationship (q-RASAR). Utilizing the comprehensive US EPA's ECOTOX database, which includes acute LC50 toxicity and chronic endpoints, we extracted crucial data such as study types, exposure routes, and chemical categories. Regression-based QSTR and q-RASAR models were developed from this dataset, emphasizing key chemical descriptors. Lipophilicity and unsaturation were significant for predicting acute toxicity, while electrophilicity, nucleophilicity, and molecular branching were crucial for chronic toxicity predictions. Additionally, q-RASAR models integrated with the "intelligent consensus" algorithm were employed to enhance predictive accuracy. The performance of these models was rigorously compared across various approaches. These refined models not only predict the toxicity of untested compounds but also reveal underlying structural influences. Validation through comparison with existing literature affirmed the relevance and robustness of our approach in ecotoxicology.

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. 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. 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.

Elucidating Molecular Mechanism and Chemical Space of Chalcones through Knowledge Graph and Machine Learning: A Combined Bio-Cheminformatic Pipeline

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models (https://github.com/RatulChemoinformatics/QSAR) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90AA1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90AA1, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90AA1 and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

Synthesis, quantum chemical insights, vibrational spectral elucidation, insecticide likeness and docking analysis of the insecticide active novel molecule 2-(4-nitrophenyl)-(3,5-difluoro-pyridine-6-yl)-1,3,4-oxadiazole

Oxadiazole derivatives containing pyridine moiety were biologically active molecules that have been widely applied in recent years in research on pesticides, particularly insecticides. In this work, a newly synthesized 2-(4-nitrophenyl)-(3,5-difluoro-pyridine-6-yl)-1,3,4-oxadiazole compound was characterized using spectroscopic techniques. The quantum chemical computations based on density functional theory were employed to investigate the molecular configuration of title compound. The modes of vibrations of the compound were identified through potential energy distribution, and the results were consistent with the experimental infrared and Raman data. Natural bond orbital assessment evaluated the significant donor-acceptor interactions within the molecule. The frontier molecular orbital analysis was performed to obtain global reactivity parameters. The molecular electrostatic potential was used to visualize the chemical reactivity and charge distribution of the molecule. From the nuclear magnetic resonance spectra, the carbon atoms bonded with nitrogen and oxygen atoms show the up-shielded peaks. The chemical implication of molecule was explained using electron localization function and localized orbital locator analysis. Moreover, the molecular docking studies performed for the newly synthesized compound revealed an efficient interaction with the acetylcholine receptor and exhibited better binding affinity than commercialized insecticides. Whilst, the physicochemical properties were assessed using the insectiPAD webtool to comprehend the insecticidal properties of title compound. Finally, the quantitative structure activity relationship study of 2-(4-nitrophenyl)-(3,5-difluoro-pyridine-6-yl)-1,3,4-oxadiazole showed higher activity (pLC50 = −1.43) against Mythimna separata, as compared to commercial insecticide avermectin (pLC50 = −1.47). These results provided that title compound could be used as a template for future insecticide investigations.

Comprehensive accurate prediction of critical jet fuel properties with multiple machine learning models

Quantitative structure–property relationship (QSPR) model development driven by emerging machine learning (ML) shows promise for accelerating design and preparation of jet fuels with complex hydrocarbon compositions. In this work, we collected 104 jet fuels from different refineries, determined the detailed components of the fuel composition, and tested the fuel properties (density, viscosity, net heat of combustion, freezing point and flash point) using standard methods to form a database of molecule structure/composition and properties. Subsequently, six mainstream ML algorithms were adopted to establish the QSPR models, in which the prediction accuracy of the best ML models for each property is improved to above 0.93. Finally, the best ML property models are applied to predict unseen RP-3 fuels, and all prediction errors are within acceptable limits. This effort not only provides valuable data for the construction of the jet fuel database, but also provides tools for predicting its critical properties.

Exploration of Aflatoxin B1 Degradation Products via Kocuria rosea: Structure Elucidation and Toxicity Analysis

Aflatoxin B1, a natural mycotoxin produced by Aspergillus fungi with high toxicity and carcinogenicity to humans and animals, has attracted more and more attention in the past 40 years. In the study of the biological detoxification of aflatoxin B1—although it has been confirmed that Kocuria rosea has the ability to efficiently remove aflatoxin B1—the degradation products, degradation pathways, and toxicity of the degradation products of aflatoxin B1 have not been clarified. Therefore, in this study, it was found that the functional groups of aflatoxin B1 changed after being cultured with Kocuria rosea, indicating the production of aflatoxin B1 degradation products. Ten main AFB1 degradation products (including aflatoxicol, aflatoxin D1, and aflatoxin D2) were identified, and their structures and fragmentation mechanisms were further elucidated by the parent ions and fragment ions of the products. The possible degradation pathway of aflatoxin B1 was proposed according to the structure of the degradation products. Additionally, the toxicity of the degradation products was analyzed according to the quantitative structure–activity relationship theory, and cytotoxicity experiments and dead–live cell staining experiments showed that the toxicity of the degradation products was significantly less than that of aflatoxin B1. In this study, the mechanism of aflatoxin B1 degradation by Kocuria rosea was explored from several perspectives, indicating that aflatoxin B1 degradation by Kocuria rosea is a promising biological method.

Evaluating applicability domain of acute toxicity QSAR models for military and industrial chemical risk assessment

Quantitative Structure-Activity Relationship (QSAR) models can be used to predict the risk of novel and emergent chemicals causing adverse health outcomes, avoidance of which is crucial for military operations. While QSAR modeling approaches have been proposed for military and industry risk assessment, the applicability of peer-reviewed tissue-specific QSAR models in military and industrial contexts remain largely unexplored, particularly with respect to specific organ toxicity. We investigated the applicability domain (AD) of acute and sub-chronic tissue-specific QSAR models to evaluate the coverage of military- and industrial-relevant chemicals. Our analysis reveals that military-relevant compounds occupy a similar chemical space as industrial compounds. However, published models for acute target organ toxicity had minimal coverage of the military and industrial chemicals. The published Collaborative Acute Toxicity Modeling Suite (CATMoS) acute oral toxicity model was the notable exception, as it covers a broad range of military and industrial chemicals. Our study underscores the urgent need for development of novel tissue-specific QSAR models, or modification of existing models, to improve chemical risk prediction in both industrial and military applications.

A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)

Quantitative structure–activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. A total of 36 single ML models and 12 consensus models were developed. The results indicated that models employing concentration addition (CA), independent action (IA), and molecular descriptors (MD) as variables demonstrated superior predictive abilities. The consensus model combining SVM and RF algorithms (labeled as CM0) demonstrated the highest level of accuracy in fitting the data, with a coefficient of determination of 0.980. Additionally, it showed strong predictive abilities when tested with external data, achieving an external R2 value of 0.945 and a Concordance Correlation Coefficient of 0.967. This study provides a positive contribution to the ecological risk assessment of a mixture of azole fungicides.

ML-based Read-Across Structure-Property Relationship (RASPR) strategy for predicting protein resistance of self-assembled monolayers (SAMs) as anti-biofouling materials

Designing highly effective antifouling materials is essential in many medical applications like implantable devices, regenerative medicine, biosensors etc. The experimental design based on a trial and error strategy of antifouling materials is difficult and requires a huge amount of time and money. Machine learning (ML) based read across strategy is a relatively novel strategy that may speed up the discovery of novel biofouling-resistant surfaces against protein adsorption making them suitably used in different medical applications. In this study, we developed data-driven machine learning-based quantitative read-across structure-property relationship (q-RASPR) models for protein adsorption data (in the case of fibrinogen and lysozyme) of self-assembled monolayers (SAMs) as antifouling materials by utilizing a mix of linear and non-linear algorithms, including Multiple Linear Regression (MLR), AdaBoost (ADB), Support Vector Regression (SVR), Linear Support Vector Regression (LSVR), Ridge Regression (RR), Random Forest (RF), Gradient Boost (GB), and Extreme Gradient Boost (XGB). The performances of all models were compared for their acceptable levels of goodness of fit, robustness, and external predictivity. The best q-RASPR models demonstrate superior performances (internal and external validation parameters) than the corresponding QSPR models and are interpreted concerning crucial molecular descriptors controlling the anti-biofouling property enabling further modifications of previous antifouling SAMs to novel antifouling SAMs with promising properties. Overall, the study demonstrates the effectiveness of the machine learning-based q-RASPR approach over the QSPR approach to understand the fundamental structure-anti-biofouling property relationships systematically with the potential to expedite the discovery of novel antifouling materials.

Local QSAR based on quantum chemistry calculations for the stability of nitrenium ions to reduce false positive outcomes from standard QSAR systems for the mutagenicity of primary aromatic amines

BackgroundPrimary aromatic amines (PAAs) present significant challenges in the prediction of mutagenicity using current standard quantitative structure activity relationship (QSAR) systems, which are knowledge-based and statistics-based, because of their low positive prediction values (PPVs). Previous studies have suggested that PAAs are metabolized into genotoxic nitrenium ions. Moreover, ddE, a relative-energy based index derived from quantum chemistry calculations that measures the stability nitrenium ions, has been correlated with mutagenicity. This study aims to further examine the ability of the ddE-based approach in improving QSAR mutagenicity predictions for PAAs and to develop a refined method to decrease false positive predictions.ResultsInformation on 1,177 PAAs was collected, of which 420 were from public databases and 757 were from in-house databases across 16 laboratories. The total dataset included 465 Ames test-positive and 712 test-negative chemicals. For internal PAAs, detailed Ames test data were scrutinized and final decisions were made using common evaluation criteria. In this study, ddE calculations were performed using a convenient and consistent protocol. An optimal ddE cutoff value of -5 kcal/mol, combined with a molecular weight ≤ 500 and ortho substitution groups yielded well-balanced prediction scores: sensitivity of 72.0%, specificity of 75.9%, PPV of 65.6%, negative predictive value of 80.9% and a balanced accuracy of 74.0%. The PPV of the ddE-based approach was greatly reduced by the presence of two ortho substituent groups of ethyl or larger, as because almost all of them were negative in the Ames test regardless of their ddE values, probably due to steric hindrance affecting interactions between the PAA and metabolic enzymes. The great majority of the PAAs whose molecular weights were greater than 500 were also negative in Ames test, despite ddE predictions indicating positive mutagenicity.ConclusionsThis study proposes a refined approach to enhance the accuracy of QSAR mutagenicity predictions for PAAs by minimizing false positives. This integrative approach incorporating molecular weight, ortho substitution patterns, and ddE values, substantially can provide a more reliable basis for evaluating the genotoxic potential of PAAs.

Developing a consistent model for predicting equilibration in polymeric passive samplers across various HOC classes in sediment pore water.

The use of polymeric passive samplers (often polydimethylsiloxane, PDMS, or low-density polyethylene, LDPE) to indicate water concentrations of hydrophobic organic compounds under environmental conditions generally requires two key parameters, a compound-specific polymer-water equilibrium partition coefficient (), and the degree of equilibration achieved in a given environmental exposure scenario. Herein, we have developed model to extrapolate equilibration of performance reference compounds between polymer and pore water across different compound classes that is also dependent upon accurate estimates of . We have also developed quantitative structure-activity relationship (QSAR) models, to estimate for PAHs, PCBs, DDx, and dioxin/furans for the two most common polymeric samplers, PDMS and LDPE. The QSAR models developed in this study provide high accuracy consistent estimates for across the different HOC families. The root mean square error of the QSAR for was 0.226 log units based upon measured values for 159 compounds and 0.184 log units for based upon measured values for 131 compounds.

Development of hybrid models by the integration of the read-across hypothesis with the QSAR framework for the assessment of developmental and reproductive toxicity (DART) tested according to OECD TG 414

The governing laws mandate animal testing guidelines (TG) to assess the developmental and reproductive toxicity (DART) potential of new and current chemical compounds for the categorization, hazard identification, and labeling. In silico modeling has evolved as a promising, economical, and animal-friendly technique for assessing a chemical's potential for DART testing. The complexity of the endpoint has presented a problem for Quantitative Structure-Activity Relationship (QSAR) model developers as various facets of the chemical have to be appropriately analyzed to predict the DART. For the next-generation risk assessment (NGRA) studies, researchers and governing bodies are exploring various new approach methodologies (NAMs) integrated to address complex endpoints like repeated dose toxicity and DART. We have developed four hybrid computational models for DART studies of rodents and rabbits for their adult and fetal life stages separately. The hybrid models were created by integrating QSAR features with similarities-derived features (obtained from read-across hypotheses). This analysis has identified that this integrated method gives a better statistical quality compared to the traditional QSAR models, and the predictivity and transferability of the model are also enhanced in this new approach.

Optimizing structure-property models of three general graphical indices for thermodynamic properties of benzenoid hydrocarbons

Cheminformatics is an interdisciplinary field that combines principles of chemistry, computer science, and information technology to process, store, analyze, and interpret chemical data. One area of cheminformatics is quantitative structure–property relationship (QSPR) modeling which is a computational approach that correlates the structural attributes of chemical compounds with their physical, chemical, or biological properties to predict the behavior and characteristics of new or untested compounds. Structure descriptors deliver contemporary mathematical tools required for QSPR modeling. One of a significant class of such descriptors is graph-based descriptors known as graphical descriptors. A degree-based graphical descriptor/invariant of a υ-vertex graph Ω=(VΩ,EΩ) has a general structure GDd=∑ij∈EΩπdegxi,degxj, where π is bivariate symmetric map, and degxi is the degree of vertex xi∈VΩ. For α∈R∖{0}, if π=(degxi×degxj)α (resp. π=(degxi+degxj)α, then GDd is called the general product-connectivity PCα (resp. sum-connectivity SCα) index of Ω. Moreover, the general Sombor index SOα has the structure π=(degxi2×degxj2)α. By choosing the heat capacity ΔH and the entropy E as representatives of thermodynamic properties, we in this paper find optimal value(s) of α which deliver the strongest potential of the predictors GDd∈{PCα,SCα,SOα} for predicting ΔH and E of benzenoid hydrocarbons. In order to achieve this, we employ tools such as discrete optimization and multivariate regression analysis. This, in turn, study completely solves two open problems proposed in the literature.

Visible-light driven S-scheme Bi2WO6/graphitic carbon nitride heterojunction for efficient simultaneous removal of TC and Cr(VI)

The application of photocatalysts in water governance has attracted extensive attention, and past research efforts often focused on single pollutant removal. Simultaneous removal of multiple pollutants from wastewater by a single processing unit is highly desirable, realistic, and challenging. The use of band-matched semiconductors to form heterojunctions is an important way to achieve this technology. In this work, we have designed a Bi2WO6/g-C3N4 heterojunction via an in-situ growth method. The in-suit XPS was used to verify the electron transfer pathway which conforms to the S-scheme mechanism. Under visible light irradiation, the removal rates of Cr(VI) and TC are 97.7 % and 96.0 % respectively. In addition to the excellent photocatalytic performance, the catalyst showed a high degree of stability during the five cycles. The formation mechanism of S-scheme heterojunction is discussed based on DFT calculation and physicochemical characterization. Liquid chromatography-mass spectrometry (LC-MS) and Quantitative Structure-Activity Relationship (QSAR) were used to analyze and predict the degradation pathways, intermediates, and toxicity. The efficient performance of the photocatalyst is attributed to the more efficient photogenerated charge transfer efficiency brought about by the formation of the built-in electric field, and the improvement of the photogenerated carrier recombination rate to provide more reactive sites with a large specific surface area. We provide a practicable strategy for the efficient treatment of inorganic and organic co-pollutants in environmental water by constructing an S-scheme heterojunction photocatalyst.

A GNN-Based QSPR Model for Surfactant Properties

Surfactants are among the most versatile molecules in the chemical industry because they can self-assemble in bulk solutions and at interfaces. Predicting the properties of surfactant solutions, such as their critical micelle concentration (CMC), limiting surface tension (γcmc), and maximal packing density (Γmax) at water–air interfaces, is essential to their rational design. However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, γcmc, and Γmax. Ninety-two surfactant data points, encompassing all types of surfactants—anionic, cationic, zwitterionic, and nonionic—are fed into the model, covering a temperature range of [20–30 °C], which contributes to its generalization across all surfactant types. We show that our models have high accuracy (R2 = 0.87 on average in tests) in predicting the three parameters across all types of surfactants. The effectiveness of the QSPR model in capturing the variation of CMC, γcmc, and Γmax with molecular design parameters are carefully assessed. The curated dataset, developed model, and critical assessment of the developed model will contribute to the development of improved surfactants QSPR models and facilitate their rational design for diverse applications.

Integrated computational approaches for identification of potent pyrazole-based glycogen synthase kinase-3β (GSK-3β) inhibitors: 3D-QSAR, virtual screening, docking, MM/GBSA, EC, MD simulation studies.

Glycogen synthase kinase-3β (GSK-3β) has emerged as a crucial target due to its substantial contribution in various cellular processes. Dysfunctional GSK-3β activity can lead to ion channel disturbances, sustain abnormal excitability, and contribute to the pathogenesis of epilepsy and other GSK-3β-related disorders. A set of 82 pyrazole analogs was utilized to study its structural features using a three-dimensional quantitative structure-activity relationship (3D-QSAR), virtual screening, molecular docking, and molecular dynamics. The QSAR model, validated using internal and external methods, demonstrated robustness with a high correlation coefficient r2training = 0.99, cross-validation coefficient q2 = 0.79, r2test = 0.69, and r2external = 0.74. The "Average of Actives" in the Activity Atlas model identified 17 molecules as active. Subsequent pharmacophore-based virtual screening of 17 actives yielded 70 compounds, which were selected as the prediction set to determine the potential GSK-3β inhibitors. Docking studies pinpointed compound P66 as the promising lead compound, with a docking score of - 10.555kcal/mol. These findings were further supported by electrostatic potential (ESP), electrostatic complementarity (EC), and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analyses. Furthermore, a 500ns molecular dynamics (MD) simulation confirmed the structural and conformational stability of the lead complex throughout the simulation period. As a result, this study suggests that compound P66 holds the potential to be a potent lead candidate for the inhibition of GSK-3β, offering a novel therapeutic approach for GSK-3β related disorders, including epilepsy.