Quantitative Structure-activity Relationship Models Research Articles

Initial Development of Automated Machine Learning-Assisted Prediction Tools for Aryl Hydrocarbon Receptor Activators

Background: The aryl hydrocarbon receptor (AhR) plays a crucial role in immune and metabolic processes. The large molecular diversity of ligands capable of activating AhR makes it impossible to determine the structural features useful for the design of new potent modulators. Thus, in the field of drug discovery, the intricate nature of AhR activation necessitates the development of novel tools to address related challenges. Methods: In this study, quantitative structure–activity relationship (QSAR) models of classification and regression were developed with the objective of identifying the most effective method for predicting AhR activity. The initial dataset was obtained by combining the ChEMBL and WIPO databases which contained 978 molecules with EC50 values. The predictive models were developed using the automated machine learning platform mljar according to a 10-fold cross validation (10-CV) testing procedure. Results: The classification model demonstrated an accuracy value of 0.760 and F1 value of 0.789 for the test set. The root-mean-squared error (RMSE) was 5444, and the coefficient of determination (R2) was 0.208 for the regression model. The Shapley Additive Explanations (SHAP) method was then employed for a deeper comprehension of the impact of the variables on the model’s predictions. As a practical application for scientific purposes, the best performing classification model was then used to develop an AhR web application. This application is accessible online and has been implemented in Streamlit. Conclusions: The findings may serve as a foundation in prompting further research into the development of a QSAR model, which could enhance comprehension of the influence of ligand structure on the modulation of AhR activity.

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

Machine learning-driven prediction of EBNA1 inhibitors against Epstein–Barr virus in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), particularly prevalent in regions such as Malaysia, is a significant health concern often linked to Epstein-Barr virus (EBV) infection. The EBV nuclear antigen 1 (EBNA1), crucial for EBV survival and NPC tumorigenicity, has emerged as a potential therapeutic target for EBV-positive NPC. In this study, we utilized quantitative structure-activity relationship (QSAR) models to predict potential inhibitors of EBNA1. These models were developed based on the molecular fingerprints of known EBNA1 inhibitors, using both classification and regression approaches. Our QSAR classification models demonstrated consistently high precision, recall, F1 score, and accuracy scores across the training set. The top-performing models, constructed using logistic regression algorithms, achieved perfect precision scores of 1.000 in the test set evaluation. These models’ recall, F1 score, and accuracy scores were 0.571, 0.727, and 0.667, respectively. On the other hand, the best-performing model among the regression models was built using the sequential minimal optimization regression algorithm, achieving a correlation coefficient of 0.703. The mean absolute error and root mean square error of this QSAR regression model were 0.173 and 0.217, respectively, whereas the relative absolute error was 0.689. We screened the enamine advanced compound library using this regression model to predict compounds with potential EBNA1 inhibitory effects. This led to the identification of the top 10 compounds with the most promising predicted EBNA1 inhibitory properties.

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.

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.

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.

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.

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.

Identification of novel DNA Gyrase inhibitor by combined Pharmacophore modeling, QSAR analysis, Molecular docking, Molecular dynamics, ADMET and DFT approaches

DNA gyrase, an ATP-dependent enzyme, plays a critical role in DNA replication, transcription, and recombination in Mycobacterium tuberculosis (MTB). While fluoroquinolones are effective antibacterial agents targeting DNA gyrase, their clinical use is often limited due to side effects and the emergence of bacterial resistance. In this study, we developed a quantitative structure-activity relationship (QSAR) model to predict the anti-tubercular activity of Quinoline-Aminopiperidine derivatives targeting the DNA gyrase enzyme, using a dataset of 48 compounds obtained from the literature. The QSAR model was validated using both internal and external validation metrics. Model 4, the best predictive model, demonstrated a strong fit with an R² of 0.8393, an adjusted R² (R²adj) of 0.8010, and a lack of fit (LOF) parameter of 0.0626. The QSAR results revealed that DNA gyrase inhibition is significantly influenced by factors such as partition coefficient, molecular flexibility, hydrogen bonding potential, and the presence of fluorine atoms. Twelve quinoline-aminopiperidine derivatives were designed, and their anti-tubercular activity was predicted using QSAR model-4. These compounds were further assessed for pharmacokinetic properties, toxicity, and binding affinity to DNA gyrase. Pharmacophore modeling was also performed and validated using MOE software. The final pharmacophore model includes the features of two aromatic hydrophobic features, one hydrogen bond acceptor, and one hydrogen bond donor. The results indicated that designed compounds QA-3 and dataset compounds C-34 exhibit favorable drug-likeness properties. Molecular dynamics simulations confirmed the stable binding of compounds QA-3 and C-34 to the DNA gyrase protein, highlighting their potential as promising anti-tubercular agents. The developed QSAR Model-4 will facilitate the prediction of anti-tubercular activity in Quinoline-Aminopiperidine derivatives.

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.

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.

Non-targeted screening and toxicity study of safety risk substances in facial skincare products: Molecular networking and computational toxicology strategy

BackgroundRisk substances in cosmetics have long been associated with adverse reactions. However, as risk substances become more concealed and diversified, conventional targeted analysis methods are no longer sufficient to meet regulatory requirements. ObjectiveTo construct a rapid and effective non-targeted screening method for the identification of risk substances, and to provide a high-throughput method for toxicity assessment. MethodsMolecular networking was utilized for the non-targeted screening of risk substances in facial skincare products, and the toxicity of these risk substances was evaluated through molecular docking and quantitative structure-activity relationship (QSAR) models. ResultsThrough molecular networking, we identified seven known prohibited ingredients, six of which were confirmed using standard substances. In addition, 17 potential risk substances were discovered within molecular clusters containing prohibited ingredients, including antibiotics, antihistamines, and phthalates, etc. Notably, chloramphenicol base and N-desmethyl chlorpheniramine exhibited stronger binding affinity to keratin 5/14 than chloramphenicol and chlorpheniramine through molecular docking, respectively. Additionally, toxicity prediction results indicated that the carcinogenicity of antibiotics presented gender differences in mice and rats, and two chlorpheniramine derivatives also showed carcinogenicity in mice. Moreover, of the 24 compounds, 11 showed skin sensitization, while 14 caused skin irritation. Furthermore, half of these compounds demonstrated potential developmental toxicity, and only 4-nitrobenzenethiol was found to be mutagenic. ConclusionIn this study, we developed a visualization strategy for non-targeted screening of risk substances and a high-throughput method for initial toxicity assessment of risk substances.

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

Despite new therapies against malaria, this disease remains one of the main causes of death affecting humanity. The phenomenon of resistance has caused concern as the drugs no longer have the same efficacy, forcing scientific research to develop new methods that prospect new molecules. With the advancement of artificial intelligence, it becomes possible to apply machine learning (ML) techniques in the discovery and evaluation of new molecules, by employing the quantitative structure-activity relationship (QSAR), a classic method that uses regressions to create a model that allows identifying and evaluating new drug candidates. This work combined QSAR with ML and developed a supervised model that modeled a fourth degree polynomial equation capable of identifying new drug candidates derived from the triclosan compound-a classic inhibitor of Plasmodium falciparum growth, the cause of severe malaria. The model produces an R 2 greater than 80% for training and concurrent testing, as well as a correlation index greater than 80% between the calculated and experimental pEC50 (negative logarithm of half maximal effective concentration) data. In addition, a web software (PlasmoQSAR) was created that allows researchers to calculate the EC50 (half maximal effective concentration) of new molecules using the developed analytical method.

Novel "Phenyl-Pyrazoline-Oxadiazole" Ternary Substructure Derivatives: Synthesis, Insecticidal Activities, and Structure-Activity Relationship Study.

In recent years, isoxazole insecticides or parasiticides targeting the γ-aminobutyric acid receptor, such as fluralaner or fluxametamide, featured a novel chemical structure and exhibited potent insecticidal activity with no-cross resistance. Thus, many research institutes have tried to modify the structures of these agents to find a new insecticide. Previously, the majority of researchers stuck to the "phenyl-isoxazole-phenyl" structure, making modifications only to other components. In this study, the "phenyl-isoxazole-phenyl" ternary motif was modified for the first time based on bioisosterism theory. A series of new derivatives carrying pyrazoline and 1,3,4-oxadiazole moieties were designed and synthesized to investigate their insecticidal activities against the diamondback moth (Plutella xylostella) and fall armyworm (Spodoptera frugiperda). Preliminary bioassay data showed that some of the target compounds exhibited good insecticidal activities against P. xylostella and S. frugiperda. Especially, compound A21 showed insecticidal activity against P. xylostella (LC50 = 1.2 μg/mL) better than commercial insecticide ethiprole (LC50 = 2.9 μg/mL) but worse than parasiticide fluralaner (LC50 = 0.5 μg/mL). Similarly, compound A21 exhibited insecticidal activity to S. frugiperda (LC50 = 13.2 μg/mL) better than commercial insecticide fipronil (LC50 = 78.8 μg/mL) but worse than fluralaner (LC50 = 0.7 μg/mL). Compound A21 could serve as a potential lead compound to control P. xylostella and S. frugiperda. The three-dimensional quantitative structure-activity relationship model revealed that the further introduction of an electron-donating group in the 2- or 3-site may increase the insecticidal activity of A21. Molecular dynamics simulations showed that the hydrogen bond of A21 and receptor was important for the binding receptor. This study has identified a new substructure called "phenyl-pyrroline-oxadiazole" instead of the previously known "phenyl-isoxazole-phenyl" substructure, offering a useful guide for the design of novel insecticide molecules.

Emodin derivatives as novel potent DPP-4 inhibitors: Design, synthesis, and in vitro evaluation

Dipeptidyl peptidase-4 (DPP-4) inhibitors have gained recognition as effective agents for lowering blood sugar levels, significantly improving glycemic control for individuals with type 2 diabetes mellitus (T2DM). Emodin, an anthraquinone derived from the traditional herbs rhubarb (Rheum officinale) and Polygonum cuspidatum, has been identified as an important component in the development of new treatments for diabetes. In the present work, we explored the DPP-4 inhibitory activity of emodin derivatives. This study focused on the design, synthesis, and evaluation of emodin derivatives for their in vitro DPP-4 inhibitory activity. Molecular docking studies indicated that 3-o-toluoyl emodin (OTEM) had the lowest docking score (−134.073) against the DPP-4 protein among the tested compounds. OTEM also achieved the highest drug-likeness score of 0.56 and demonstrated DPP-4 inhibitory activity, with an IC50 value of 0.77 μM. Furthermore, structure-activity relationship (SAR) analysis suggested that the addition of an ortho-toluoyl group at the C-3 position could enhance DPP-4 inhibition. Additionally, quantitative structure-activity relationship (QSAR) model assessments revealed that log P was the only descriptor significantly influencing DPP-4 inhibitory activity. Therefore, the current study indicates that OTEM could serve as a promising lead compound to address the demand for antidiabetic agents.

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

Design, Synthesis, and 3D-QASR of Oxazoline Derivatives Containing an S-S Moiety as Potential Acaricide.

To mitigate the detrimental effects of agricultural pest mites on crop yield, an active substructure splicing strategy was employed to modify etoxazole by introducing an S-S moiety. The target products were obtained efficiently via a bilateral disulfurating reagent (DSMO), which was developed by our group. The leaf dip method was used to evaluate the activities of the designed target compounds against the eggs and larvae of the spider mite (Tetranychus cinnabarinus). Most of the target compounds exhibited good efficacy in controlling the larvae and eggs of T. cinnabarinus. Based on these results, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model was established to guide the construction of compound 7l. Notably, compound 7l exhibited a better activity against T. cinnabarinus eggs (LC50 = 0.0035 mg/L) compared to etoxazole (LC50 = 0.2990 mg/L). Greenhouse bioassays indicated that compound 7l exhibits excellent acaricidal activity against egg of T. cinnabarinus, which is better than the etoxazole at 1.0 mg/L. Additionally, some of the compounds showed inhibitory effects against Dickeya zeae (D. zeae), Xanthomonas campestris pv campestris (Xcc), Xanthomonas oryzae pvoryza (Xoo), and Xanthomonas oryzae pvoryzicola (Xoc). Furthermore, compounds 7l not only exhibited relatively potent against Plutella xylostella activities (LC50 = 24.0 mg/L) but also had low toxicity (LC50 > 11.0 μg/bee) to Apis mellifera. In conclusion, the current experimental results suggest that oxazoline derivatives containing an S-S moiety have the potential to serve as lead compounds for the development of novel acaricide agents.

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of pharmaceutical use during pregnancy. Those undergoing pregnancy are often excluded from clinical drug trials due to the risk that participation would pose to their health and the health of the developing fetus. However, they often require pharmaceuticals to manage health conditions that, if left 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 as inputs to make predictions concerning the outcome of interest using computational algorithms. Models are validated and evaluated rigorously with metrics such as accuracy, area under the receiver operator curve, sensitivity, and precision. 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 the 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 the outcome of interest and combination of such algorithms and software.

Cheminformatics-driven prediction of BACE-1 inhibitors: Affinity and molecular mechanism exploration

The β-secretase, sometimes referred to as BACE1 or Asp2, is the enzyme responsible for initiating the production of Aβ by breaking down the amyloid precursor protein. Hence, BACE is a pivotal target for pharmacological intervention aimed at diminishing the production of Aβ in Alzheimer's disease (AD). We did a quantitative structure-activity relationship (QSAR) study on 1235 compounds that had been experimentally reported as BACE-1 inhibitors (Ki) to find the target molecule and structural patterns linked to blocking the BACE-1 receptor. The OECD-recommended genetic algorithm-multiple linear regression (GA-MLR) QSAR model strikes a good balance between being able to make accurate predictions and understanding how things work. It achieves high values for many evaluation metrics, including R2tr = 0.8047, Q2LMO = 0.802, R2ex = 0.805, CCCex = 0.891, Q2-F1=0.801, Q2-F2=0.799, and Q2-F3=0.815. The mechanistic interpretation of QSAR has identified some nitrogen atoms that are required to block beta-secretase and make it less effective at doing its job. A positively charged aromatic carbon atom has a more significant impact on beta-secretase-1 inhibition. The docking study showed that the catalytic dye residues Asp32 and Asp228 become protonated when compound 27 is present, but they stay unprotonated when compound 27 is not present. These rings of pyrazine and pyridine interact hydrophobically with Tyr71 and Ile118 residues, confirming the pharmacophoric features seen in the QSAR data. We examined the stability of both the protein-ligand complex and the apo-protein. The apoprotein had an average gyration radius of 22.13, whereas the protein-ligand complex had an average gyration radius of 22.91. No changes were made to the results from the molecular docking, molecular dynamics (MD) simulation, MMGBSA (Molecular Mechanics Generalized Born Surface Area), or DFT analyses. Therefore, the current work could effectively contribute to the future development of BACE inhibitors in medication design.