Quantitative Structure-activity Relationship Models Research Articles

Predicting the Time-Dependent Toxicities of Binary Mixtures of Five Antibiotics to Vibrio qinghaiensis sp.-Q67 Based on the QSAR Model.

Antibiotics may be exposed in a mixed state in natural environments. The toxicity of antibiotic mixtures exhibits time-dependent characteristics, and data on the time-dependent toxicity of antibiotic mixtures is also relatively lacking. In this study, the toxicities of 45 binary mixtures composed of five antibiotics were investigated against Vibrio qinghaiensis sp.-Q67 (Q67) at multiple exposure times (4, 6, 8, 10, and 12 h). Quantitative structure-activity relationship (QSAR) models were developed for predicting the time-dependent toxicities of 45 binary mixtures. The results showed that the best QSAR models presented coefficient of determination (R 2) of (0.818-0.913) and explained variance in prediction leave-one-out (Q 2 LOO) of (0.781-0.894) and predictive ability (Q 2 F1, Q 2 F2, Q 2 F3 > 0.682, concordance correlation coefficient > 0.859). The R 2 values of QSAR models outperformed the R 2 (0.628-0.810) of the conventional concentration addition models and the R 2 (0.654-0.792) of the independent action models. Furthermore, the QSAR models showed higher R 2 and Q 2 LOO values at 4 h compared to other exposure times. Specifically, the model at the 30% effective concentration (EC30) had R 2 of 0.902 and Q 2 LOO of 0.883, while the model at the 50% effective concentration (EC50) had R 2 of 0.913 and Q 2 LOO of 0.894. The CATS2D_04_DP descriptor was found to be the most dominant and negatively correlated factor influencing the toxicity of mixed antibiotics against Q67 in the nine QSAR models developed over five exposure times. The reduction in the number of DP pharmacophore point pairs with a topological distance of 4 in the represented molecules is the primary cause for the rise in the time-dependent toxicity of the antibiotics against Q67.

Oil extraction desulfurization based on ionic liquid: Back-extractant screening, experiment and process simulation

A comprehensive back-extractant screening method was first developed for regenerating ionic liquids (ILs) after oil extraction desulfurization (EDS). Statistical analysis was performed using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with the Entropy Weighting Method (EWM) on screening indices obtained based on the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) and the Quantitative Structure Activity Relationship (QSAR) models. CCl4 was selected as the optimal back-extractant among 196 candidates for regenerating the IL, 1-butyl-3-methylpyridinium dicyanamide ([BMPy][DCA]), targeting the simultaneous removal of thiophene (TS), benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyldibenzothiophene (4,6-DMDBT). The experimentally measured hexary liquid–liquid equilibrium (LLE) confirmed the satisfactory selectivities and distribution coefficients of CCl4. Based on the NRTL parameters correlated from the LLE data, a complete EDS process including IL-extraction and IL-regeneration was established and evaluated economically based on the total annual cost (TAC). The results demonstrated that this process significantly decreased the sulfur content from 500 ppm to less than 10 ppm. The study results verified the feasibility and effectiveness of the proposed method and showed the excellent industrial performance of the selected IL and back-extractant.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure–activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis.

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

Benchmarking of BMDC assay and related QSAR study for identifying sensitizing chemicals

The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle.This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method.The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.

Application of machine learning to predict unbound drug bioavailability in the brain

Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.

Detailed DFT/MD simulation, QSAR modeling, electrochemical, and surface morphological studies of self-assembled surfactants as eco-friendly corrosion inhibitors for copper in 1 M HNO3 solution

Three novel self-assembled phenyl pyrazole cationic surfactant derivatives, coded as SA-6, SA-12, and SA-18, were synthesized, structure confirmed, and examined as eco-friendly corrosion inhibitors for Cu in 1 M HNO3 electrolyte using open circuit potential (OCP), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS) measurements. The efficacy of these surfactants was pointedly influenced by dosage (5 × 10−5 M − 1 × 10−3 M), attaining a maximum of 90.9 %, 92.9 %, and 94.5 % for SA-6, SA-12, and SA-18, respectively, at 1 × 10−3 M. The fact that the highest shift in corrosion potential (Ecorr) is so minor suggests that the compounds under investigation are inhibitors of the mixed type (anodic/cathodic). The high value of Kads and the negative ΔGadso value (−35.0, −35.8, and −36.8 kJ mol−1) suggests that the SA-6, SA-12, and SA-18 inhibitors have a strong and spontaneous tendency to adsorbed onto the Cu substrate. The development of the self-assembled surfactant defensive film on the Cu substrate was morphologically verified by atomic force microscopy (AFM). The studied self-assembled surfactants adsorbed physically and chemically according to Langmuir isotherm model. Quantitative structure–activity relationship (QSAR) was used to construct the prediction model for SA-6, SA-12, and SA-18′s anti-corrosion properties, while density functional theory (DFT) calculations were used to determine the chemical descriptors linked to the frontier molecular orbitals (FMOs). It was shown that the results of the computational and electrochemical experiments were highly consistent.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Cytotoxicity of emerging halophenylacetamide disinfection byproducts in drinking water: Mechanism and prediction

Halophenylacetamides (HPAcAms) have been identified as a new group of nitrogenous aromatic disinfection byproducts (DBPs) in drinking water, but the toxicity mechanisms associated with HPAcAms remain almost completely unknown. In this work, the cytotoxicity of HPAcAms in human hepatoma (HepG2) cells was evaluated, intracellular oxidative stress/damage levels were analyzed, their binding interactions with antioxidative enzyme were explored, and a quantitative structure-activity relationship (QSAR) model was established. Results indicated that the EC50 values of HPAcAms ranged from 2353 μM to 9780 μM, and the isomeric structure as well as the type and number of halogen substitutions could obviously induce the change in the cytotoxicity of HPAcAms. Upon exposure to 2-(3,4-dichlorophenyl)acetamide (3,4-DCPAcAm), various important biomarkers linked to oxidative stress and damage, such as reactive oxygen species, 8‑hydroxy-2-deoxyguanosine, and cell apoptosis, exhibited a significant increase in a dose-dependent manner. Moreover, 3,4-DCPAcAm could directly bind with Cu/Zn-superoxide dismutase and induce the alterations in the structure and activity, and the formation of complexes was predominantly influenced by the van der Waals force and hydrogen bonding. The QSAR model supported that the nucleophilic reactivity as well as the molecular compactness might be highly important in their cytotoxicity mechanisms in HepG2 cells, and 2-(2,4-dibromophenyl)acetamide and 2-(3,4-dibromophenyl)acetamide deserved particular attention in future studies due to the relatively higher predicted cytotoxicity. This study provided the first comprehensive investigation on the cytotoxicity mechanisms of HPAcAm DBPs.

Discovery of Trisubstituted N-Phenylpyrazole Containing Diamides with Improved Insecticidal Activity.

To increase the structural diversity of insecticides and meet the needs of effective integrated insect management, the structure of chlorantraniliprole was modified based on a previously established three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The pyridinyl moiety in the structure of chlorantraniliprole was replaced with a 4-fluorophenyl group. Further modifications of this 4-fluorophenyl group by introducing a halogen atom at position 2 and an electron-withdrawing group (e.g., iodine, cyano, and trifluoromethyl) at position 5 led to 34 compounds with good insecticidal efficacy against Mythimna separata, Plutella xylostella, and Spodoptera frugiperda. Among them, compound IV f against M. separata showed potency comparable to that of chlorantraniliprole. IV p against P. xylostella displayed a 4.5 times higher potency than chlorantraniliprole. In addition, IV d and chlorantraniliprole exhibited comparable potencies against S. frugiperda. Transcriptome analysis showed that the molecular target of compound IV f is the ryanodine receptor. Molecular docking was further performed to verify the mode of action and insecticidal activity against resistant P. xylostella.

Synthesis, antifungal, antibacterial activity, and computational evaluations of some novel coumarin-1,2,4-triazole hybrid compounds

Effective plant pathogen control presents an important challenge. Pesticide resistance and associated health and environmental problems demonstrate the need for novel, safe active ingredients for plant protection. Since both coumarins and 1,2,4-triazoles show pesticidal activity, in this study, we evaluated the antimicrobial activity of coumarin-1,2,4-triazole hybrids against plant pathogenic fungi (Fusarium oxysporum, Fusarium culmorum, Macrophomina phaseolina and Sclerotinia sclerotiorum), bacterial plant pathogens (Pseudomonas syringae and Rhodococcus fascians), and beneficial bacteria (Bacillus mycoides and Bradyrhizobium japonicum). Coumarin-1,2,4-triazoles inhibited the growth of S. sclerotiorum and F. oxysporum, while no antibacterial effect on either pathogens or soil-beneficial bacteria was observed. A quantitative structure–activity relationship models for antifungal activities on S. sclerotiorum and F. oxysporum, developed using Dragon descriptors, can explain 79% and 77% of the compounds inhibitory activity, respectively. According to molecular docking, title compounds are potential sterol 14α-demethylase inhibitors, with 7-((5-mercapto-4-(p-tolyl)-4H-1,2,4-triazol-3-yl)methoxy)-4-methyl-2H-chromen-2-one as the promising candidate for further research in plant protection.

Prediction of ionic liquids toxicity using machine learning models for application to gas hydrate

AbstractIonic liquids (ILs) are highly favored in the oil and gas industry as gas hydrate inhibitors due to their dual functionality as thermodynamic inhibitor and kinetic hydrate inhibitor. Though known as the “green alternatives,” concerns about the effects of ILs in the environment are rising such that ILs can stabilize in water systems. Furthermore, there are insufficient data on the toxicity of ILs, limiting the use of ILs for industrial applications. Ridge, LASSO, decision tree, random forest, extra tree, gradient boost, and support vector regressions were used to develop IL toxicity predictive models. Random forest yielded the strongest predictive performance, scoring the highest R2 value of 0.86, with mean absolute error and root mean square error values of 0.32 and 0.43, respectively. Feature selections were conducted to investigate the contributions of the five molecular descriptors involved in developing regression models in this work. Descriptor MSDC was found to contribute the highest at 67% in predicting the toxicity of ILs, followed by SNarA and MAXDPC, demonstrating contributions of 15.2% and 14.1%, respectively. Further quantitative structure–activity relationship model validations were executed; the use of three descriptors resulted in a 2% increase in predictive performance for decision tree regression, whereas R2 values remained the same for random forest, extra tree, and gradient boosting.

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.Graphical

Identification of the single and combined acute toxicity of Cr and Ni with Heterocypris sp. and the quantitative structure-activity relationship (QSAR) model.

Mining wastewater with heavy metals poses a serious threat to the ecological environment. However, the acute single and combined ecological effects of heavy metals, such as chromium (Cr) and nickel (Ni), on freshwater ostracods, and the development of relevant prediction models, remain poorly understood. In this study, Heterocypris sp. was chosen to investigate the single and combined acute toxicity of Cr and Ni. Then, the quantitative structure-activity relationship (QSAR) model was used to predict the combined toxicity of Cr and Ni. The single acute toxicity experiments revealed high toxicity for both Cr and Ni. In addition, Cr exhibited greater toxicity compared to Ni, as evidenced by its lower 96-hour half-lethal concentration (LC50) of 1.07 mg/L compared to 4.7 mg/L for Ni. Furthermore, the combined acute toxicity experiments showed that the toxicity of Cr-Ni was higher than Ni but lower than Cr. Compared with the concentration addition (CA) and independent action (IA) models, the predicted results of the QSAR model were more consistent with the experimental results for the Cr-Ni combined acute toxicity. So, the high accuracy of QSAR model identified its feasibility to predict the toxicity of heavy metal pollutants in mining wastewater.

Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow.

Molecular features play an important role in different bio-chem-informatics tasks, such as the Quantitative Structure-Activity Relationships (QSAR) modeling. Several pre-trained models have been recently created to be used in downstream tasks, either by fine-tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM-2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different-dimensional embeddings derived from the ESM-2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order toguarantee fair analyses. As a result, the 640- and 1280-dimensional embeddings derived from the 30- and 33-layer ESM-2 models, respectively, are the most valuablesince statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM-2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM-2 model. Frequency studies revealed that only a portion of the ESM-2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state-of-the-art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non-DL based QSAR models yield comparable-to-superior performances to DL-based QSAR models. The developed KNIME workflow is available-freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non-DL based QSAR models.

Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges.

Understanding the bioaccumulation of nanomaterials (NMs) by organisms is essential in evaluating their potential ecotoxicity. However, the experimental determination of bioaccumulation is substantially challenging, which spawned the development of prediction approaches via establishing models for various NMs. Conventional modeling approaches, such as the biotic ligand model (BLM), partition coefficients, accumulation factor models, and quantitative structure-activity relationship (QSAR) models, were initially used in the application of NMs, aiming to provide a reliable quantitative dose in a resource-saving way. These methods, which are based on the uptake patterns of substances, probably lead to deviated results due to the different uptake behaviors of NMs. In this study, currently developed models to evaluate the bioaccumulation of NMs are critically reviewed, with their feasibilities and limitations being analyzed. In addition, the recently developed machine learning amended models have taken great efforts in realizing biological behaviors of NMs in organisms by providing in silico predictions. Though this data-driven approach has limitations in mechanism exploration, it may give different insights into the bioaccumulation model establishment and critical feature identification.

Novel 5-Sulfonyl-1,3,4-thiadiazole-Substituted Flavonoids as Potential Bactericides and Fungicides: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship Studies.

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae, Rhizoctonia solani, and Colletotrichum orbiculare. Compounds 1, 3, 7, 9, 13, and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC50 values below 10 μg/mL, which were superior to bismerthiazol (70.89 μg/mL). Compound 2 (EC50 = 0.41 μg/mL) displayed the most effective inhibitory potency against R. solani in vivo, comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35-53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani, with an EC50 value of 0.28 μg/mL and a half-maximal effective concentration of 0.46 μg/mL.

Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors.

A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.

Strategic development and validation of Isoquinoline-Derived inhibitors targeting ROCK1 kinase

Rho-associated coiled-coil containing kinases (ROCKs) have emerged as pivotal downstream effectors of small guanosine triphosphatases. In recent years, isoquinoline derivatives have garnered substantial attention as potent inhibitors of ROCK1 (RKIs). To delve deeper into their pharmacological properties and to identify potential inhibitors, we conducted an in-depth exploration of a series of RKIs derived from isoquinoline derivatives. This exploration encompassed a multifaceted approach, combining three-dimensional quantitative structure–activity relationship modeling, induced fit docking, and virtual screening. Our comparative molecular field analysis model (q2 = 0.609, R2 = 0.991, ONC = 6, and r2pred = 0.973) and the most robust comparative molecular similarity indices analysis model (q2 = 0.712, R2 = 0.998, ONC = 7, and r2pred = 0.89) exhibited commendable predictive accuracy, bolstered by robust validation parameters. To gain deeper insights into the mechanism of action of isoquinoline-based RKIs, we harnessed precise docking techniques, including induced fit docking, structural interaction fingerprints, and binding pose metadynamics simulations. Employing the PRRR_2 pharmacophore, we meticulously screened Enamine's HLL-460 compound library, unearthing thousands of isoquinoline compounds that aligned with the desired profile. Subsequently, we identified three promising compounds (DS01, DS02, and DS03) through Glide docking, each assessed at varying precision levels. These compounds underwent comprehensive evaluation for their drug-like properties, leveraging QikProp and SwissADME, ultimately unveiling their potential as novel and efficacious RKIs. Furthermore, bolstered by molecular dynamics simulations and validated through activity assays, the bioactivity of the screened molecules DS02, and DS03 was corroborated. DS02 and DS03, previously unreported for their activity against ROCK1, delivered exceptionally promising results. This comprehensive study not only elucidates validated strategies for drug development but also imparts invaluable insights into the design of potent RKIs.

Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis

Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4′-Dichlorobiphenyl (PCB-15), 2,2′,4,4′,6,6′-Hexachlorobiphenyl (PCB-155), and 2,2′,3,3′,4,4′,5,5′,6,6′-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = −1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = −1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.