QSAR Research Articles

QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Computer prediction of toxicity of new S-alkyl derivatives of 1,2,4-triazole

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

Analysis of octane isomer properties via topological descriptors of line graphs

Currently, graph-based molecular structure descriptors are frequently used in QSAR (Quantitative Structure-Activity Relationship) and QSPR (Quantitative Structure-Property Relationship) modeling of the properties of chemical compounds. Octane isomers have an important role in the testing of such relationships. In this study, we compute some well-known topological descriptors of the molecular graphs of isomeric octanes and their line graphs, both indices and coindices. Then we analyze the correlation coefficients between the physicochemical properties of the octane isomers and some well-known topological indices and their coindices of the molecular graphs and the line molecular graphs of the octane isomers, to find the particular topological descriptors that have the best predictive values.

Quantitative structure–activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity

The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure–activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity. The results demonstrate high predictive performance across multiple targets, with balanced accuracy exceeding 0.80 for the majority of models. Furthermore, stability checks confirmed the consistency of predictive performance across multiple training-test splits. The results obtained by using QSAR predictions to identify known markers of adversities highlighted the utility of the models for risk assessment and for prioritizing compounds for further experimental evaluation.Scientific contributionThe work describes the development of QSAR models as tools for screening chemicals with potential systemic toxicity, thus contributing to resource savings and providing indications for further better-targeted testing. This study provides advances in the field of computational modeling of MIEs and information from AOP which is still relatively young and unexplored. The comprehensive modeling procedure is highly generalizable, and offers a robust framework for predicting a wide range of toxicological endpoints.

Designing the next generation of polymers with machine learning and physics-based models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( < 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R 2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

A Comparative Study: Domination Parameters and Physico-Chemical Properties of Benzenoid Hydrocarbons

In graph theory, a graph is a mathematical structure that consists of a set of vertices and a set of edges connecting those pairs of vertices. Let G = ( V , E ) be a graph, a dominating set for G is a subset D ⊆ V of vertices such that every vertex in V ∖ D has at least one neighbor in D. The domination number of G, denoted by γ ( G ) , is the minimum cardinality of a dominating set in G. Recently, Khan (2023) performed a comparative study of seven important domination parameters with the π -electronic energy of benzenoid hydrocarbons, with a new theoretical approach. Physico-chemical properties are crucial for understanding the physical and chemical behavior of a molecule. In this paper, we perform a comparative study of those seven domination parameters with the physico-chemical properties of benzenoid hydrocarbons. We find the values of those domination parameters for the 22 lower benzenoid hydrocarbons and follow the statistical approach described by Khan (2023). The two physico-chemical properties to be chosen are the normal boiling point T b , a representative for intermolecular and van-der-Waals type interactions, whereas, the standard enthalpy/heat of formation Δ H f o is referred to represent the thermal properties of a molecular graph. This study further enhances that the performance of paired domination number is exceptional among all other parameters and highly correlated with both of the physico-chemical properties. Its correlation coefficient is 0.9981 (respectively, 0.9726) with T b (respectively, Δ H f o ). The outstanding correlation coefficient of paired domination number ensures further usage in quantitative structure-property relationship (QSPR) models.

Comparative evaluation and QSAR modeling of developmental neurotoxicity of novel brominated flame retardants in zebrafish

The novel brominated flame retardants (NBFRs) have received wide concerns due to their ubiquitous occurrence in the environment and their potential risks to ecosystems and human health. However, the toxicity data of NBFRs are still lacking, especially their toxicity comparison data, and toxicity predictions for untested NBFRs are extremely limited. In this study, eight commonly used NBFRs and decabromodiphenyl ether (BDE209) were selected to compare their toxicity at concentrations between 0.03 and 3.69 μM, by exposing zebrafish embryos until 120 h post-fertilization (hpf) and evaluating 18 toxicity indicators including basic development indicators and a series of behavioral indicators. The toxicity potency of the tested compounds ranked by the total number of significantly affected endpoints were pentabromobenzene (PBB) ≈ 2,4,6-tribromophenol (TBP) > BDE209 ≈ bis(2-ethylhexyl) tetrabromophthalate (TBPH) > pentabromotoluene (PBT) ≈ 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EHTBB) > 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE) > hexabromobenzene (HBB) > decabromodiphenyl ethane (DBDPE). Almost all the tested compounds affected the locomotor behavior of zebrafish larvae, suggesting that the refined behavioral indicators were sensitive endpoints. Furthermore, the quantitative structure-activity relationship (QSAR) model we developed suggested that molecular surface area (MSA) might be the critical factor for determining the developmental neurotoxicity of NBFRs to zebrafish larvae, except for congeners with larger molecules (e.g. DBDPE, BTBPE). These findings would contribute to elucidating the toxicity differences among various NBFRs and provide important references for their toxicity prediction.

Verification of the interaction between human bitter taste receptor T2R46 and polyphenols; Computational chemistry approach

Recent studies have indicated that the activation of bitter taste receptors (T2R) expressed in gastrointestinal secretory cells has a regulatory effect on the secretion of gastrointestinal hormones. Polyphenols are known to be ingested at a daily intake of 5 g or more and commonly have a bitter taste. Consequently, the interaction between the bitter taste receptor T2R46 and 490 polyphenols was investigated using in silico simulation techniques. It was demonstrated that W883.32 and E2657.39 play a pivotal role in the recognition of polyphenols and known ligands by T2R46, with frequent interactions observed, particularly with flavonoids. The results of the quantitative structure-activity relationship (QSAR) analysis demonstrated a high degree of correlation (R2=0.9359) between polyphenols and T2R46 in a model that incorporated molecular interaction field regions and branching scales. Furthermore, known ligands were also found to fit this model (R2=0.9155). These findings suggest that polyphenols may act as T2R46 ligands.

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.

An automated computational data pipeline to rapidly acquire, score, and rank toxicological data for ecological hazard assessment.

Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;20:2203-2217. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Application of computer-aided drug design in drug discovery and development: Updating knowledge

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition

Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥ 8 (IC50 values ≤ 10 nM). Our svm-based ensemble model predicted IC50 values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

Selective Metal‐Coordinated Nanodrugs Based on Thiazine Moiety for Anticancer Therapeutics: Spectroscopic, Theoretical, and Molecular Docking Studies

ABSTRACTThe current study involved production, comprehensive structural analysis, and physicochemical characterizing of two distinctive complexes namely, Cu(TBH) and Zn(TBH); TBH donor ligand: N′‐(1‐(3,6‐dihydro‐4‐hydroxy‐2,6‐dioxo‐2H‐1,3‐thiazin‐5‐yl)ethylidene)‐2‐hydroxybenzohydrazide) using a wide range of analytical methods, including elemental analysis, UV–Vis, FT‐IR, and 1HNMR spectrometry, molar conductivity and magnetic susceptibility measurements, and thermal analysis. According to the findings, chelation can occur through O, N, and O donor atoms of monoanionic chelator for generating mono‐nuclear chelates with tetrahedral geometry for Cu (II) and octahedral geometry for Zn (II). The anticarcinogenic ability of TBH chelator and its coordinated compounds against human liver cancer (HepG‐2) was investigated. The Cu(TBH) complex was found to have selective and promising anticancer activity against a liver cancer cell line, with lower IC50 (liver carcinoma cell line) and higher IC50 (normal human cell line) values than other produced compounds and standard drugs. Utilizing DFT computations, the molecular structures of the TBH and its complexes were verified, offering a detailed understanding of their quantum chemical characteristics. A quantitative structure–activity relationship (QSAR) model, which illustrates the association between DFT‐computed descriptors and biological activities pIC50, was also created using multiple linear regression (MLR) on the synthesized compounds as anticancer medicines. Additionally, there are no issues with the produced compounds' oral bioavailability according to (ROF) Lipinski's rule of five. Furthermore, docking studies of the synthesized TBH chelator and its chelates with CDK2 kinase have been performed to validate the biological findings. According to our findings, the novel Cu(TBH) nanodrug exhibits considerable cytotoxic activity, prompting further study into its pharmacological profile and exploring its potential in drug development.

Ornidazole degradation based on peroxymonosulfate activation induced by oxygen vacancies (OV)-enriched Cu-Co-TiO2: Coexistence of free-radical and non-radical pathways

Employing transition-metal catalysts in peroxymonosulfate (PMS) activation reactions to facilitate combined free-radical and non-radical interactions is regarded as a proficient approach for decomposing organic contaminants. However, the creation of active catalysts faces significant challenges due to low activation efficiency, inadequate action sites, and instability of current activation materials. Here, we successfully prepared bimetallic Cu and Co co-doped TiO2 (Cu-Co-TiO2) catalyst using the sol-gel technique. Excellent ornidazole (ONZ) removal efficiency (0.628 min−1) was demonstrated by the Cu-Co-TiO2/PMS system, which was applicable across a broad pH range (4−10) and unaffected by different water matrices. Moreover, the coexistence of free-radical (SO4•-, •OH and O2•−) and non-radical (1O2) routes in the Cu-Co-TiO2/PMS system, where SO4•- and 1O2 are the predominant active substances, was confirmed by quenching experiments and electron paramagnetic resonance (EPR) study. The synergistic impact of Cu/Co bimetal may hasten the redox cycles of Cu+/Cu2+ and Co2+/Co3+, causing plenty of oxygen vacancies (OV) and improving PMS activation efficiency. The quantitative structure-activity relationship (QSAR) examination of the intermediates showed a decrease in toxicity, and the potential pathways of ONZ degradation were illustrated using Liquid Chromatography Mass Spectrometry (LC-MS) technology. This work not only demonstrated the effectiveness and stability of Cu-Co-TiO2 as a PMS activator, but it also offered fresh information on how to remove organic pollutants from wastewater by using PMS activation via the radical and non-radical oxidation pathway.

Optimizing predictive models for evaluating the F-temperature index in predicting the π-electron energy of polycyclic hydrocarbons, applicable to carbon nanocones

In the fields of mathematics, chemistry, and the physical sciences, graph theory plays a substantial role. Using modern mathematical techniques, quantitative structure-property relationship (QSPR) modeling predicts the physical, synthetic, and natural properties of substances based only on their chemical composition. For a chemical graph, the temperature of a vertex is a local property introduced by Fajtlowicz (1988). A temperature-based graphical descriptor is structured based on temperatures of vertices. Involving a non-zero real parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}, the general F-temperature index Tβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{\\beta }$$\\end{document} is a temperature index having strong efficacy. In this paper, we employ discrete optimization and regression analysis to find optimal value(s) of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} for which the prediction potential of Tβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{\\beta }$$\\end{document} and the total π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi$$\\end{document}-electron energy Eπ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E_{\\pi }$$\\end{document} of polycyclic hydrocarbons is the strongest. This, in turn, answers an open problem proposed by Hayat & Liu (2024). Applications of the optimal values for Tβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{\\beta }$$\\end{document} are presented a two-parametric family of carbon nanocones in predicting their Eπ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E_{\\pi }$$\\end{document} with significantly higher accuracy.

Aromatase as a novel target of parabens in human and rat placentas: 3D-quantitative structure-activity relationship and docking analysis

Aromatase (CYP19A1), a pivotal enzyme in the biosynthesis of estradiol from testosterone, is predominantly expressed in reproductive tissues including placentas. This study investigated the effects of paraben acid and nine parabens on the activity of human and rat CYP19A1 using microsomes derived from human and rat placentas and on estradiol secretion in human choriocarcinoma BeWo cells. The results showed that propyl, butyl, hexyl, heptyl, and nonyl parabens significantly inhibited human CYP19A1 activity, with IC50 values of 66.37, 61.08, 55.65, 48.26, and 27.24 μM, respectively. In BeWo cells, these parabens notably diminished estradiol secretion at concentrations of 100 μM. Similarly, rat CYP19A1 was inhibited by these parabens, with IC50 values of 98.07, 70.10, 41.30, 27.93, and 6.33 μM for propyl, butyl, hexyl, heptyl, and nonyl parabens, respectively. Kinetic analysis identified these compounds as mixed inhibitors. Bivariate correlation analysis revealed a negative correlation between the partition coefficient value, molecular weight, the number of carbon atoms in the alcohol moiety, as well as heavy atom number and IC50 values. Three-dimensional quantitative structure-activity relationship analysis highlighted the critical role of hydrophobic regions in determining inhibitory potency. Docking studies suggested that parabens interact with the heme-iron binding site of both human and rat CYP19A1. This study elucidates the inhibitory effects of various parabens on CYP19A1 and their binding mechanisms, thereby providing a deeper understanding of their potential impact on estrogen biosynthesis.

Photocatalytic hydrogen production and sulfamerazine degradation via a novel dual S-scheme photocatalyst: Nanocomposite synthesis, characterization and mechanism insights

Creating highly effective photocatalysts is crucial for harnessing solar energy to degrade pollutants and produce hydrogen (H₂). In this study, we successfully synthesized a novel dual S-scheme iron oxide (Fe₂O₃)/bismuth oxide (Bi₂O₃)/titanium dioxide (TiO₂) ternary photocatalyst using a straightforward method. This photocatalyst was employed for efficient photocatalytic water splitting and the degradation of the antibiotic sulfamerazine (SMZ) under visible light. Various characterization and photoelectrochemical techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), Brunauer–Emmett–Teller surface area analysis (BET), photocurrent measurements, Mott-Schottky analysis, photoluminescence (PL), electrochemical impedance spectroscopy (EIS), and electron spin resonance (ESR), were utilized to analyze the synthesized materials. Among the synthesized nanocomposites, the 15 wt% Fe₂O₃/Bi₂O₃/TiO₂ (15FeBi/TiO₂) composite demonstrated exceptional photocatalytic efficiency, achieving 98 % SMZ degradation and a hydrogen production rate of 590.36 μmol/g·h. Experimental results, including scavenging tests and ESR findings, highlighted the crucial role of hydroxyl radicals (•OH) and superoxide radicals (•O₂−) in the photocatalytic process. Moreover, liquid chromatography-mass spectrometry (LC-MS) results proposed three degradation pathways, and quantitative structure-activity relationship (QSAR) analysis showed that the toxicity of intermediates was effectively reduced. The 15FeBi/TiO₂ photocatalyst also exhibited excellent reusability, retaining about 85 % of its initial activity after five cycles, and proved effective against various pollutants and in real water matrices. This research contributes to the design and development of high-activity heterojunction photocatalysts for superior clean energy generation and pollutant degradation under visible light.

Understanding the potential of coal gangue as photocatalyst for antibiotic degradation: The role of abundant oxygen vacancies and electron-hole pairs

Coal gangue (CG) offers promising potential as a cost-effective photocatalyst for antibiotic degradation. However, current approaches do not emphasize the origin of its photocatalytic activity. This study investigates the photocatalytic activity of CG through a comparative analysis with one-pot aerobic/anaerobic calcinated CG. The potential application of CG as a photocatalyst for tetracycline (TC) decontamination in water was evaluated under dark and light. Characterization results identified anatase and kaolinite as the critical photosensitive minerals in CG. Batch removal experiments indicated that CG achieved up to 98 % TC removal at 1 g/L dosage, with most biotoxic intermediates reduced to below 0.9 mg L−1 Lethal Concentration 50 (LC50) of Fathead minnow (Pimephales promelas). Degradation pathways identified by density functional theory (DFT) and quantitative structure-activity relationship (QSAR) indicated that superoxide anion (⁎O2−) and electron-hole (e−-h+) pairs were predominant contributors to TC photodegradation. Theoretical calculations suggest that the oxygen vacancies on the CG surface can accelerate the production of ⁎O2−, while fast electron transport channels within surface hydroxyl groups facilitate e− and h+ separation. The findings of this research elucidate the origin of CG's photocatalytic activity and highlight the potential to serve as a photocatalyst for low-cost antibiotic removal, offering a promising solution for coal waste management and utilization.

Application of Machine Learning in the Development of Fourth Degree Quantitative Structure–Activity Relationship Model for Triclosan Analogs Tested against Plasmodium falciparum 3D7

Application of Machine Learning in the Development of Fourth Degree Quantitative Structure–Activity Relationship Model for Triclosan Analogs Tested against <i>Plasmodium falciparum</i> 3D7