Two-dimensional Quantitative Structure-activity Relationship Research Articles

Synthesis, antifungal evaluation, two-dimensional quantitative structure-activity relationship and molecular docking studies of isoxazole derivatives as potential fungicides.

Sheath blight and bakanae disease, prominent among emerging rice ailments, exert a profound impact on rice productivity, causing severe impediments to crop yield. Excessive use of older fungicides may lead to the development of resistance in the pathogen. Indeed, a pressing and immediate need exists for novel, low-toxicity and highly selective fungicides that can effectively combat resistant fungal strains. A series of 20 isoxazole derivatives were synthesized using alkoxy/halo acetophenones and N,N-dimethylformamidedimethylacetal. These compounds were characterized by various spectroscopic techniques, namely 1H nuclear magnetic resonance (NMR), 13C NMR and liquid chromatography-high-resolution mass spectrometry, and were evaluated for their fungicidal activity against Rhizoctonia solani and Fusarium fujikuroi. Compound 5n (5-(2-chlorophenyl) isoxazole) exhibited highest activity (effective dose for 50% inhibition [ED50] = 4.43 μg mL-1) against R. solani, while 5p (5-(2,4-dichloro-2-hydroxylphenyl) isoxazole) exhibited highest activity (ED50 = 6.7 μg mL-1) against F. fujikuroi. Two-dimensional quantitative structural-activity relationship (QSAR) analysis, particularly multiple linear regression (MLR) (Model 1), highlighted chi6chain and DistTopo as the key descriptors influencing fungicidal activity. Molecular docking studies revealed the potential of these isoxazole derivatives as novel fungicides targeting sterol 14α-demethylase enzyme, suggesting their importance as crucial intermediates for the development of novel and effective fungicides. All test compounds were effective in inhibiting both fungi, according to the QSAR model, with various descriptors, such as structural, molecular shape analysis, electronic and thermodynamic, playing an important role. Molecular docking studies confirmed that these compounds can potentially replace commercially available fungicides and help control fungal pathogens in rice crops effectively. © 2024 Society of Chemical Industry.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to treat Metabolic Syndrome.

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism. To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome. In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor. The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 µM and 0.0027 µM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed. The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Identification of potent small-molecule PCSK9 inhibitors based on quantitative structure-activity relationship, pharmacophore modeling, and molecular docking procedure

Abstract Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) attaches to the domain of LDL receptor (LDLR), diminishing LDL-C influx and LDLR cell surface presentation in hepatocytes, resulting in higher circulating LDL-C levels. PCSK9 dysfunction has been linked to lower levels of plasma LDLC and a decreased CHD risk. Purpose To identify a potent small-molecule PCSK9 inhibitor in compounds that are currently being studied in clinical trials. Methods We first performed chemical absorption, distribution, metabolism, excretion, and toxicity filtering of 9800 clinical trial compounds obtained from the ZINC 15 database using Lipinski's rule of five and achieved 3853 compounds. Two-dimensional (2D) Quantitative Structure-Activity Relationship (QSAR) was initiated by computing molecular descriptors and selecting important descriptors of 23 PCSK9 inhibitors. Multivariate calibration was performed with the partial least square regression (PLS) method with 18 compounds for training to design the QSAR model and five compounds for the test set to assess the model. The best latent variables (LV) (LV=6) with the lowest value of Root-Mean-Square Error of Cross-Validation (RMSECV) of 0.48 and leave-one-out cross-validation correlation coefficient (R2CV)=0.83 were obtained for the QSAR model. The low RMSEC (0.21) with high R²cal (0.966) indicates the probability of fit between the experimental data and the calibration model. Results Using QSAR analysis of 3853 compounds, 2635 had a pIC50<1 and were considered for pharmacophore screening. The PHASE module designed the pharmacophore hypothesis through multiple ligands. The top 14 compounds (pIC50>1) were defined as active, whereas nine (pIC50<1) were considered as an inactive set. Three five-point pharmacophore hypotheses achieved the highest score: DHHRR1, DHHRR2, and DHRRR1. The highest and best model with survival scores (5.365) was DHHRR1, comprising one hydrogen donor (D), two hydrophobic groups (H), and two rings of aromatic (R) features. We selected the molecules with a higher 1.5 fitness score (257 compounds) in pharmacophore screening (DHHRR1) for molecular docking screening.Molecular docking indicates that ZINC000051951669, with a binding affinity: of -13.2 kcal/mol and two H-bonds, has the highest binding to the PCSK9 protein. ZINC000011726230 with energy binding: -11.4 kcal/mol and three H-bonds, ZINC000068248147 with binding affinity: -10.7 kcal/mol and one H-bond, ZINC000029134440 with a binding affinity: -10.6 kcal/mol and four H-bonds were ranked next, respectively. Conclusion The archived molecules identified as inhibitory PCSK9 candidates, and especially ZINC000051951669 may therefore significantly inhibit PCSK9 and should be considered in the newly designed trials.Figure 1.The six-top docking compound against PCSK9 with lowest binding affinity. A) ZINC000051951669, B) ZINC000011726230, C) ZINC000068248147, D) ZINC000029134440, E) ZINC000000602086, F) ZINC000034630885.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Development of a 2D-QSAR Model forTissue-to-Plasma PartitionCoefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (Kp) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the Kp value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma. Instead of experiments, many researchers have sought in silico methods. Today, most of the models for Kp prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for Kp using physicochemical descriptors instead of in vivo experimental data as explanatory variables. We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1-14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for Kp value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model. Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)]. We could develop a 2D-QSAR model for Kp value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.

Mechanism analysis of synthetic musks-induced miscarriage risk in pregnant women: Molecular and adverse outcome pathways perspectives

Synthetic musks (SMs) with pleasant odor have been extensively used in personal care products. SMs are emerging contaminants due to the wide detection in human breast milk and blood, and even caused miscarriage to pregnant women. Aiming at this, the two-dimensional quantitative structure-activity relationship (2D-QSAR) coupling with three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were first used to analyze the SM-induced risk of miscarriage in pregnant women, and the above two models were used to mutually authenticate each other. It was found that the critical volume, melting point and heat of formation of SMs were the key descriptors leading to miscarriage. In addition, the adverse outcome pathway (AOP) was used to assess the miscarriage risk of selected SMs and the environment-friendly SM derivatives with desired functional properties designed in previous studies. Results showed that the miscarriage risk of musk ketone derivative (M27) was reduced by approximately three times than that of MK. The miscarriage risk of galaxolide derivatives (i.e., D6 and D24) were about six times lower than that of galaxolide, which further confirmed that the theoretically designed SMs derivatives can effectively reduce the risk of miscarriage in pregnant women. This study provides a comprehensive analysis of the mechanism of SM-induced miscarriage, and provides an accurate and rapid theoretical evaluation method for the future risk assessment of SM-induced miscarriage.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

A Rational Approach to Anticancer Drug Design: 2D and 3D- QSAR, Molecular Docking and Prediction of ADME Properties using Silico Studies of Thymidine Phosphorylase Inhibitors

Background: Cancer is the most prevalent disease seen nowadays. Thymidine phosphorylase (TP) is an angiogenic enzyme that is overexpressed in many solid tumors. Over the years, Thymidine phosphorylase has emerged as a novel target for anticancer drug development as an inhibitor. Objective: To design novel oxadiazole-isatin pharmacophore-containing molecules and explore their structural requirements related to the anticancer activity. Methods: Pharmacophore optimisation was carried out for oxadiazole-isatin hybrid molecules using molecular modeling studies (2D and 3D QSAR). Further, the new chemical entities were designed using the combilib tool of V life software. To have a better understanding of the binding interactions, the newly designed molecules were docked. To achieve a drug-like pharmacokinetic profile, molecules were also tested for ADME prediction. Results: Two-Dimensional Quantitative Structure-Activity Relationship (2D-QSAR) model was generated using the multiple regression method with r2 = 0.84 and q2 = 0.76. Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model was obtained by simulated annealing k nearest near (SA kNN) method with q2 = 0.8099. Molecular docking studies showed promising results. Compound 5 was found to be with the best dock score and the best fit to the active site pocket of the thymidylate phosphorylase enzyme. The compounds have notable absorption, distribution, metabolism, and excretion (ADME) properties that can be predicted to assure a drug-like pharmacokinetic profile. Conclusion: One of the most successful and fast-increasing methodologies is molecular modeling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. The successful use of molecular modeling was done in this study, with caution taken to avoid any chance co-relation. Optimised pharmacophore was obtained and new chemical entities were designed. Docking studies revealed that Compound 5 has shown better H-bond interaction with Lys 221 and Thr 151 with bond distances 2.0 Α° and 1.8 Α° which is the most active molecule. ADME tests discovered that the majority of the newly designed compounds were within a reasonable range as required in a druglike pharmacokinetic profile. Molecules 2, 4, 5, 6 can be considered as a lead for future synthesis and biological screening.

Design, Biological Evaluation, and Computer-Aided Analysis of Dihydrothiazepines as Selective Antichlamydial Agents.

Chlamydia trachomatis (CT) causes the most prevalent sexually transmitted bacterial disease in the United States. The lack of drug selectivity is one of the main challenges of the current antichlamydial pharmacotherapy. The metabolic needs of CT are controlled, among others, by cylindrical proteases and their chaperones (e.g., ClpX). It has been shown that dihydrothiazepines can disrupt CT-ClpXP. Based on this precedent, we synthesized a dihydrothiazepine library and characterized its antichlamydial activity using a modified semi-high-throughput screening assay. Then, we demonstrated their ability to inhibit ClpX ATPase activity in vitro, supporting ClpX as a target. Further, our lead compound displayed a promising selectivity profile against CT, acceptable cytotoxicity, no mutagenic potential, and good in vitro stability. A two-dimensional quantitative structure-activity relationship (2D QSAR) model was generated as a support tool in the identification of more potent antichlamydial molecules. This study suggests dihydrothiazepines are a promising starting point for the development of new and selective antichlamydial drugs.

Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer's disease

Alzheimer's disease (AD) is an age-related neurodegenerative disorder, which is the most common cause of dementia in elderly individuals. It is characterized by selective neuronal cell death that affects the brain area related to memory and learning. So far, various computational research targeting AD have been reported, but we are still far from finding a precise treatment strategy for AD. It appeared of interest to us to carry out a two-dimensional quantitative structure-activity relationship (2D-QSAR) analysis against multiple targets of AD using large datasets to determine the essential structural features which are responsible for the inhibition of the enzymes/targets. In the present research, we have implemented 2D-QSAR modeling against twelve major targets (AChE, BuChE, BACE1, β-amyloid, 5-HT6, CDK-5, Gamma-secretase, Glutaminyl Cyclase, GSK-3β, MAO-B, NMDA and Phosphodiester (PDE10A) enzymes) of AD for the identifications of novel multitarget inhibitors. The models were used to check the applicability domain of a pool of ∼19 million compounds obtained from the four chemical drug-like databases (ZINC12, Asinex, NCI, and InterBioscreen databases) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drug. Additionally, we have also developed the quantitative structure activity-activity relationship (QSAAR) and selectivity-based models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have also performed chemical Read-Across predictions using the Read-Across-v3.1 tool (https://dtclab.webs.com/software-tools), the results for the external validation metrics were found to be better than the 2D-QSAR-derived predictions. Furthermore, molecular docking experiments have been performed to understand the molecular interactions between ligands and enzymes at the atomic level, and the observations are compared with the structural features acquired from QSAR models that justified the mechanistic aspect of binding phenomena. The proposed models and read-across hypotheses could be used as potential tools to identify essential molecular features for designing suitable drug(s) for Alzheimer's therapy using rational design of multi-target inhibitors.

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.

Fluorine Atoms on C6H5-Corrole Affect the Interaction with Mpro and PLpro Proteases of SARS-CoV-2: Molecular Docking and 2D-QSAR Approaches.

The chymotrypsin-like cysteine protease (3CLpro, also known as main protease—Mpro) and papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been used as the main targets for screening potential synthetic inhibitors for posterior in vitro evaluation of the most promising compounds. In this sense, the present work reports for the first time the evaluation of the interaction between Mpro/PLpro with a series of 17 porphyrin analogues-corrole (C1), meso-aryl-corrole (C2), and 15 fluorinated-meso-aryl-corrole derivatives (C3–C17) via molecular docking calculations. The impact of fluorine atoms on meso-aryl-corrole structure was also evaluated in terms of binding affinity and physical-chemical properties by two-dimensional quantitative structure–activity relationship (2D-QSAR). The presence of phenyl moieties increased the binding capacity of corrole for both proteases and depending on the position of fluorine atoms might impact positively or negatively the binding capacity. For Mpro the para-fluorine atoms might decrease drastically the binding capacity, while for PLpro there was a certain increase in the binding affinity of fluorinated-corroles with the increase of fluorine atoms into meso-aryl-corrole structure mainly from tri-fluorinated insertions. The 2D-QSAR models indicated two separated regions of higher and lower affinity for Mpro:C1–C17 based on dual electronic parameters (σI and σR), as well as one model was obtained with a correlation between the docking score value of Mpro:C2–C17 and the corresponding 13C nuclear magnetic resonance (NMR) chemical shifts of the sp2 carbon atoms (δC-1 and δC-2) of C2–C17. Overall, the fluorinated-meso-aryl-corrole derivatives showed favorable in silico parameters as potential synthetic compounds for future in vitro assays on the inhibition of SARS-CoV-2 replication.

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

ABSTRACT The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Nitroaromatics as hypoxic cell radiosensitizers: A 2D-QSAR approach to explore structural features contributing to radiosensitization effectiveness

Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaromatics or compounds with missing data. In the present work, three datasets consisting of 18 nitrofurans, 11 nitrothiophenes and 84 nitroimidazoles were utilised for two-dimensional QSAR modeling to retrieve their structural features essential to elicit radiosensitivity. The work comprises two parts: (i) local modeling using individual datasets; and (ii) global modeling by clubbing the three datasets. The two-dimensional descriptors were calculated using Dragon (version 7.0) software. The developed models were obtained using various feature selection techniques applied in “Small Dataset Modeling” and “Double Cross Validation” tools available from https://dtclab.webs.com/software-tools. Finally, the models were validated using stringent metrics following the Organisation for Economic Co-operation and Development (OECD) guidelines. The developed models are robust, predictive, and are useful tools to predict the radiosensitization of newly developed nitroaromatics. Furthermore, the global model was used to predict two external sets comprising 10 and 47 compounds, and the prediction ability was validated using the “Prediction Reliability Indicator” tool.

The investigation of influencing factors on the degradation of sulfonamide antibiotics in iron-impregnated biochar-activated urea-hydrogen peroxide system: A QSAR study

Iron-impregnated biochar-activated urea-hydrogen peroxide (FB-activated UHP) is a potential in-situ technology for simultaneously reducing soil sulfonamide antibiotic contaminants and improving soil fertility. To better understand the degradation of sulfonamide antibiotics by FB-activated UHP, a two-dimensional quantitative structure-activity relationship (2D-QSAR) model based on quantum chemical parameters and a three-dimensional QSAR (3D-QSAR) model based on molecular force field were developed to investigate the factors influencing the removal efficiencies (Re%). The optimal 2D-QSAR model was Re%= 0.858–8.930 E-5 EB3LYP-0.175 f(+)x with the evaluation indices of R2= 0.732, q2= 0.571, and Qext2= 0.673. The given 2D-QSAR model indicated that the molecular size (EB3LYP) and Fukui index with respect to nucleophilic attack (f(+)) were intrinsic factors influencing Re%. Three degradation pathways were subsequently proposed based on the f(+) distribution. Compared to the 2D-QSAR model, the developed 3D-QSAR model exhibited a better predictive ability, with the evaluation indices of R2= 0.989, q2= 0.696, and SEE= 0.001. The analysis of field contribution rates suggested that electrostatic field (48.2%), hydrophobic field (25.3%), and hydrogen-bond acceptor field (12.7%) were the main factors influencing Re%. These findings generated critical information for evaluating the degradation mechanisms/rules and provided theoretical bases for initially estimating the Re% of sulfonamide antibiotics undergoing FB-activated UHP process.

A Combined approach of QSAR study, molecular docking and pharmacokinetics prediction of promising Amide-Ac6-aminoacetonitriles Cathepsin K inhibitors

• Combined use of QSAR, molecular docking and pharmacokinetics prediction approaches. • One promising Cathepsin K inhibitor was identified. Cathepsin K (Cat-K) highly expressed in osteoclasts, is considered to be a promising target for osteoclast-related maladies. In the present study, quantitative structure–activity relationships (QSAR) and molecular docking approaches were used to identify promising Cat-K inhibitors from a series of Amide-Ac6-aminoacetonitriles analogs. During the two-dimensional QSAR study, the statistical model PLS_MDL was chosen (r 2 = 0.823, q 2 (cross-validation) = 0.732). Descriptors with positive or negative contributions were derived from model PLS_MDL. In order to build robust three-dimensional QSAR models, three different molecular fingerprints were used as analysis precepts for clustering and three different minimization algorithms were employed for generation of molecular poses. The best database which used molecular fingerprint of FCFP and minimization algorithm of Smart Minimizer was selected for further QSAR model construction. The ideal CoMFA and CoMSIA models were acquired (CoMFA: r 2 = 0.963, q 2 = 0.674, r 2 pred = 0.702 and Q 2 (F2) = 0.616; CoMSIA_SEHA: r 2 = 0.989, q 2 = 0.622, r 2 pred = 0.719 and Q 2 (F2) = 0.638). Physicochemical, structural, electrostatic, and steric properties were derived from the created QSAR models and then applied to portray a design scheme. Furthermore, after a selecting of docking scheme, docking studies were carried out to measure the binding affinity of eligible query compounds and to reveal the detailed interactions between ligand and the target protein. Amongst these designed compounds, compound19OX32 was focused for its preferable predicted activity and docking score. Strong interactions and extra hydrogen bonds which echoed the previous summarized design scheme were also observed via a deep investigation of docking result. Consequently, compound 19OX32 was identified as promising Cat-K inhibitor.

QSAR Analysis of Quinazolinyl-arylurea Derivatives as Potential Anti- Cancer Agents: GA-MLR Chemometric Approach

Background: Cancer is the most common malignancy in men and women globally. The tyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intracellular signal transduction processes and play a key role in cell proliferation, differentiation, migration, metabolism, and programmed cell deaths. In this context, kinases are considered as a potential drug target for cancer therapy. Methods: In the present study, a two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) was performed to analyze anticancer activities of 28 quinazolinyl-arylurea (QZA) derivatives based on the liver (BEL-7402), stomach (MGC-803), and colon (HCC-827) cancer cell lines using multiple linear regression (MLR) analysis. It was accomplished using 2D-QSAR analysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors to develop predictive models that correlate structural features of QZA derivatives to their anticancer activities. A suitable set of molecular descriptors, such as constitutional, topological, geometrical, electrostatic, and quantum-chemical descriptors were calculated to represent the structural features of compounds. The genetic algorithm (GA) method was used to identify the important molecular descriptors to build the QSAR models and used to predict the anti-cancer activities. Results and Discussion: The obtained 2D-QSAR models were vigorously validated using various statistical metrics using leave-one-out (LOO) and external test set prediction approaches. The best predictive models by MLR gave highly significant square of correlation coefficient (R2 train) values of 0.799, 0.815, and 0.779 for the training set, and the correlation coefficients (R2 test) were obtained 0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. The models also demonstrated good predictive power confirmed by the high value of cross-validated correlation coefficient Q2 value of 0.663, 0.717, and 0.671 for three different cancer cell lines. Importantly, the model's quality was judged as well based on mean absolute error (MAE) criteria, and the results were consistent with proposed limits by Golbraikh and Tropsha. Conclusion: The QSAR results of the study indicated that the proposed models were robust and free from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, and ETA_Beta_ns_d have positively contributed descriptors for anti-cancer activity in the liver, stomach, and colon cancer cell lines and a detailed mechanistic interpretation of each model revealed important structural features that were responsible for favorable or unfavorable for anticancer activity. The predictive ability of the proposed models was good and may be useful for developing more potent quinazolinyl-arylurea compounds as anti-cancer agents.

The c-Src kinase inhibitors: 2D-QSAR study by Multiple Linear Regression method

A two-dimensional quantitative structure-activity relationship (2D-QSAR) study was conducted on a series of 34 purine derivatives against c-Src tyrosine kinase. This analysis is affected by two statistical methods integrated with XLSTAT software: principal component analysis (PCA) and multiple linear regression (MLR). The best model established by multiple linear regression (R 2 =0,802, Q 2 cv=0,757, R 2 test =0.696) showed very satisfactory results. The proposed QSAR model was verified by internal and external tests, such as Y randomization test and Golbraikh-Tropsha criteria. The domain of applicability of the MLR model has been investigated using the Williams diagram to detect the compounds that are outside this domain.

Predictive Quantitative Structure-Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds.

Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.

Application of a 2D-QSAR with a sine normalization method for the biodegradation of fluoroquinolones to poison cyanobacteria.

Cyanobacteria are photosynthetic autotrophic aquatic prokaryotes. One of the methods for controlling cyanobacterial blooms is to destroy the phycobiliproteins required for photosynthesis. In this study, to improve the biodegradation of the fluoroquinolones through inhibit cyanobacteria, the molecular docking scores of 32 fluoroquinolones (FQs) with four categories of phycobiliproteins from cyanobacteria were calculated after sine normalization to characterize the binding ability between them. A two-dimensional quantitative structure-activity relationship (2D-QSAR) model was constructed based on the comprehensive scores. Danofloxacin (DAN) with the highest comprehensive score was chosen for molecular modification. When docking with four categories of phycobiliproteins from cyanobacteria, the docking values of DAN-11 and DAN-16 were increased up to 35.75%. Moreover, their functional characteristics and environmentally friendly predictive values were improved. When the DAN-11 and DAN-16 molecules docked with the other cyanobacterial phycobiliproteins, indicating that the designed DAN derivatives had general applicability to poison cyanobacteria, the weak interaction forces might increase the binding ability between the DAN derivatives and the receptor phycobiliprotein compared with the target molecule.