3D-QSAR Models Research Articles

Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors

The drugs that are the most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models for such drugs and to select a robust predictive models from the corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. The genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D QSAR model that depicted the significance of the molecular volume and the number of multiple bonds along with the presence/ absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model, which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule- protein interactions, elicited by molecular docking, corroborated with the field interactions, as revealed by 2D QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs, as potent AChE inhibitors.

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Inhibition activity of triazoles as a new family for the inhibition of the Indoleamine 2,3-dioxygenase 1 IDO1 protein using 2D-QSAR approach

Protein IDO1 (indoleamine 2,3-dioxygenase) occupies a critical position in the regulation of the immune system and is involved in cancer progression and the development of immune diseases. Being a therapeutic target for such critical diseases, we aimed to investigate the IDO1 inhibition activity of thirty-nine triazole derivatives using a quantitative structure-activity relationship. The dataset was under principal component analysis, multiple linear regression, and multiple non-linear regression from which two models were generated. The best 2D-QSAR model was generated using linear regression, demonstrating a determination coefficient of R2=0.680, a good acceptable internal cross-validated coefficient of R2cv=0.700, an error of MSE=0.074, and a good predictive potential of R2test=0.809. The QSAR model was further investigated using the applicability domain, which showed that all molecules were within the applicability domain, hence the absence of an outlier. Overall, the obtained results provide a reliable and highly predictive model for the design and prediction of new IDO1 inhibitors thereby influencing cancer progression and autoimmune disease development.

ACE inhibitors from Suaeda salsa: 3D-QSAR modeling, metabolomics, molecular docking and molecular dynamics simulations.

The online version contains supplementary material available at 10.1007/s40203-024-00233-0.

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations.

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

Molecular docking, 3D-QASR and molecular dynamics simulations of benzimidazole Pin1 inhibitors.

Pin1 (protein interacting with never-in-mitosis akinase-1) is a member of the family of peptidylprolyl cis-trans isomerases (PPIases) that specifically recognize and isomerize substrates containing phosphorylated Ser/Thr-Pro sequences. Pin1 is involved in many cellular processes and plays a key role in the cell cycle, transcriptional regulation, cell metabolism, proliferation and differentiation, and its abnormalities lead to degenerative and neoplastic diseases. Pin1 is highly expressed in human cancers and promotes the development of tumors by activating multiple oncogenes and inactivating multiple tumor suppressor genes, making it an attractive target for cancer therapy. In this study, we investigated the binding mechanism and conformational relationship between benzimidazole Pin1 inhibitors and Pin1 proteins by molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, binding free energy calculations and decomposition, and molecular dynamics simulations. Molecular docking and molecular dynamics simulations disclosed the most likely binding pose of benzimidazoles with the Pin1 protein. The results of 3D-QSAR modeling indicated that electrostatic fields, hydrophobic fields and hydrogen bonding play important roles in the binding process of inhibitors to proteins. The binding free energy calculations and energy decomposition indicated that Lys63, Arg69, Cys113, Leu122, Met130, and Ser154 may be key residues in the binding of benzimidazole-based inhibitors to the Pin1 protein. This study provides an important theoretical basis for the design and optimization of benzimidazole compounds.

Design of some potent non-toxic Autoimmune disorder inhibitors based on 2D-QSAR, CoMFA, molecular docking, and molecular dynamics investigations

Current clinical research suggests that inhibitors of protein arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) which are of biological and therapeutic interest, may show potential in treating rheumatoid arthritis, type 1 diabetes, Graves' disease and other autoimmune disorder. In the present study, a comprehensive analysis was conducted on a collection of 32 compounds concerning their anti-rheumatoid arthritis activity as inhibitors of PAD4. This analysis represents the first instance in which these compounds were computationally examined, employing an in-silico approach that considered 2D-3D QSAR modeling, and molecular docking and was further validated through molecular dynamics and ADMET properties assessment. A credible 2D QSAR (Q_LOO^2 ​= ​0.6611 and R^2 ​= ​0.7535) model was constructed and verified using an external validation test set, Y-randomization, variance inflation factor (VIF), mean effect (MF), and William's plot applicability domain (AD). Ligand-based alignment was implemented in the 3D-QSAR examination. The outcomes demonstrated that CoMFA (uvepls) (Q2LOO ​= ​0.5877; R2 ​= ​0.9983) possess remarkable stability and foresight. The internal validation indicated that CoMFA (uvepls) MIFs display superior predictive capability compared to COMFA (ffdsel). Structural criteria determined by the contour maps of the model and molecular docking simulations were strategically employed to computationally develop 10 new, non-toxic autoimmune disease inhibitors with increased efficacy. Docking tests were done on the newly developed compounds to illustrate their binding mechanism and to identify critical interaction residues inside the active region of rheumatoid arthritis (PDB id: 3BLU). In addition, docking results of the selected designed compounds inside the active sites of type 1 diabetes receptor (6DFX), and Graves' disease receptor (4QT5) demonstrated their rheumatoid arthritis (PDB id: 3BLU) selectivity. A molecular dynamics simulation and binding free energy calculations using the MM/GBSA technique confirmed the stability of the proposed compound D4 inside the rheumatoid arthritis (3BLU) receptor active site. In summary, the results of our investigation might give considerable insight into the future design and development of new autoimmune disease inhibitors.

Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives.

In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.

Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study.

α-Glucosidase enzyme inhibition is a legitimate approach to combat type 2 diabetes mellitus as it manages to control postprandial hyperglycemia. In this pursuit, a literature search identified quinoline-based molecules as potential α-glucosidase inhibitors. Thus our intended approach is to identify pharmacophoric features responsible for the α-glucosidase inhibition. This was achieved by performing, ligand-based pharmacophore modeling, 3D QSAR model development, pharmacophore-based screening of a rationally designed quinoline-based benzohydrazide Schiff base library, identifying, synthesizing and characterizing molecules (6a-6j) by IR, (1H and 13C) NMR, and mass studies. Further, these molecules were evaluated for α-glucosidase and α-amylase inhibitory potential. Compound 6c was found to inhibit α-glucosidase enzyme with an IC50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC50 value of 19.37 ± 0.96 μM as compared to acarbose (IC50: 32.63 ± 1.07 μM); the inhibitory kinetics of compounds 6b and 6c revealed a competitive type of inhibition; the inhibitory effect can be attributed to its mapped pharmacophoric feature and model validation with a survival score of 5.0697 and vector score of 0.9552. The QSAR model showed a strong correlation with an R 2 value of 0.96. All the compounds (6a-6j) showed no toxicity in L929 cell lines by the MTT assay method. Further, the binding orientation and stability of the molecules were assessed using molecular docking studies and MD trajectory analysis. The energy profile of the molecules with protein as a complex and molecules alone was evaluated using MM/GBSA and DFT calculations, respectively; finally, the pharmacokinetic profile was computed using ADMET analysis.

Synthesis of azepane-acridine heterocyclic adducts assisted by the catalytic influence of γ-Fe2O3@TiO2-Vitamin B1 and evaluation of their medicinal potential through in vitro antimicrobial, QSAR, and molecular docking studies

Present work elicits synthesis of novel azepane-acridines, assisted by titania-encapsulated γFe2O3 nanoparticles supported on Vitamin B1 (γFe2O3@TiO2-Vitamin B1) as a new magnetically recyclable heterogeneous nano-catalytic system. The reaction was accomplished using biocompatible PEG-200 (Polyethylene glycol) as the solvent under ultrasonic irradiations. The structure and morphology of synthesized magnetic nano-catalyst was ascertained based on data obtained from powder X-ray diffraction (PXRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDAX), Fourier transform infrared (FT-IR), and VSM (Vibrating-sample magnetometer) TGA, DTA and DTG techniques. The characteristic size of the prepared heterogenous nano-catalyst is 34.62 nm. Also, the structures of all the synthesized azepane-acridine heterocyclic scaffolds were corroborated through spectro-analytical data. These new compounds were found to have remarkable biological activity profile against the bacterium S.aureus with IC50 values ranging from 1.41 to 2.75 µg/mL. Further, these in-vitro studies were correlated with statistically significant 2D/3D- QSAR models. The best 2D-QSAR models reveal the positive influence of slogp and Vander Waal Surface area descriptors while the negative influence of the polar surface area and T_2_N_6 descriptors on the bioactivity with ideal values of squared correlation coefficient (r2), cross-validated correlation coefficient (q2) with high values of Fisher ratio (F-test) indicating the model to be statistically relevant. The finest 3D-QSAR models MLR (Multiple Linear Regression method) provide practical information in the characterization and differentiation of compound binding sites. Moreover, molecular docking studies were performed to evaluate the antifungal activity of synthesized scaffolds against the black fungi crystal protein, indicating remarkable bioassay scores. Atom economy, practical feasibility, cost-effectiveness, minimum reaction time, High yields, and an additive-free process are the foremost advantages of the developed methodology.

6-Aryl-4-cycloamino-1,3,5-triazine-2-amines: synthesis, antileukemic activity, and 3D-QSAR modelling.

Despite significant progress in immunotherapy and chimeric antigen receptor T cell therapy of leukemia, chemotherapy is the major treatment option for the disease. Therefore, the development of potent and safe drugs for standard and targeted chemotherapy of leukemia remains an important task for medicinal chemists. A library of 94 diverse 6-aryl-4-cycloamino-1,3,5-triazine-2-amines was prepared using a one-pot microwave-assisted protocol, which involves a three-component reaction of cyanoguanidine, aromatic aldehydes and cyclic amines, and subsequent dehydrogenative aromatization of the dihydrotriazine intermediates in the presence of alkali. The cytotoxic properties of prepared compounds were evaluated against the leukemic Jurkat T cell line and the selectivity of the 24 most active compounds was also assessed using a normal fibroblast MRC-5 cell line, indicating selective antiproliferative activity against leukemic cells. The structure-activity relationship was analysed, and the prepared 3D-QSAR model was found to predict the antileukemic activity of the compounds with reasonable accuracy. In the cell morphology study, both apoptosis and necrosis features were observed in Jurkat T cells after treatment with the most active compound.

Design, 3D-QSAR, molecular docking, ADMET, molecular dynamics and MM-PBSA simulations for new anti-breast cancer agents

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.

Effects of 1,4-dihydropyridine derivatives on cell injury and mTOR of HepG2 and 3D-QSAR study

1,4-dihydropyridine derivatives (1,4-DHPs) are a class of drugs used to treat cardiovascular diseases, but these drugs can cause liver injury. To reveal the toxicity characteristics of these compounds, we used a series of assays, including cell viability, enzyme activity detection, and western blotting, to investigate the toxicity of seven kinds of 1,4-DHPs (0–100 μM) on HepG2 cells and establish 3D-QSAR model based on relevant toxicity data. After HepG2 cells were treated with 1,4-DHPs for 24 h, high-dose (100 μM) 1,4-DHPs decreased cell viability to varying degrees, while ROS and MDA contents were significantly increased, and ATP content was reduced. Moreover, with the concentration of 100 μM 1,4-DHPs (Nimodipine, Nitrendipine, Cilnidipine, and Manidipine) were markedly inhibited the phosphorylation levels of mTOR protein. The results of the 3D-QSAR model showed that the non-cross validation coefficient (R2) and cross validation coefficient (Q2) of the model were 0.982 and 0.652, respectively. Combined with external validation and the Williams diagram, the model showed good predictability and application domain. Based on the CoMSIA 3D contour map, the introduction of large volume and hydrogen bond receptor groups on the carbonyl oxygen side chains of the 1,4-DHPs ring 3- and 5- was beneficial for reducing the toxicity of 1,4-DHPs. The results of this study could supplement information on the cytotoxicity of 1,4-DHPs, and could provide theoretical support for predicting the toxicity of 1,4-DHPs.

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

Chalcones from plants cause toxicity by inhibiting human and rat 11β-hydroxysteroid dehydrogenase 2: 3D-quantitative structure-activity relationship (3D-QSAR) and in silico docking analysis

Chalcones from licorice and its related plants have many pharmacological effects. However, the effects of chalcones on the activity of human and rat 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2), and associated side effects remain unclear. The inhibition of 11 chalcones on human and rat 11β-HSD2 were evaluated in microsomes and a 3D-quantitative structure-activity relationship (3D-QSAR) was analyzed. Screening revealed that bavachalcone, echinatin, isobavachalcone, isobavachromene, isoliquiritigenin, licochalcone A, and licochalcone B significantly inhibited human 11β-HSD2 with IC50 values ranging from 15.62 (licochalcone A) to 38.33 (echinatin) μM. Screening showed that the above chemicals and 4-hydroxychalcone significantly inhibited rat 11β-HSD2 with IC50 values ranging from 6.82 (isobavachalcone) to 72.26 (4-hydroxychalcone) μM. These chalcones acted as noncompetitive/mixed inhibitors for both enzymes. Comparative analysis revealed that inhibition of 11β-HSD2 depended on the species. Most chemicals bind to the NAD+ binding site or both the NAD+ and substrate binding sites. Bivariate correlation analysis showed that lipophilicity and molecular weight determine inhibitory strength. Through our 3D-QSAR models, we identified that the hydrophobic region, hydrophobic aliphatic groups, and hydrogen bond acceptors are pivotal factors in inhibiting 11β-HSD2. In conclusion, many chalcones inhibit human and rat 11β-HSD2, possibly causing side effects and there is structure-dependent and species-dependent inhibition on 11β-HSD2.

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis.

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme-inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

Research on the Anti-tumor Activity of Novel Histone Deacetylase Inhibitors based on 3D QSAR Model

Background: Histone deacetylases (HDACs) pertain to the category of Zn2 + or nicotinamide adenine dinucleotide (NAD+)- dependent proteolytic enzymes. While the antitumor effect of HDAC inhibitors alone has been demonstrated and the effect of HDAC inhibitors on solid tumors is not ideal, which considerably limits their clinical use. Therefore, the search for novel HDAC inhibitors equipped with specific inhibitors is extremely urgent and necessary. Methods: 3D-QSAR was employed to investigate insights into the crucial structural element that effect the activity of novel HDAC small molecule inhibitors. The best saliency CoMFA and CoMSIA models are obtained using 55 molecules in the training set and 16 molecules in the test set. Results: The statistical quality of the generated model is demonstrated by internal and external cross-validations. The CoMFA model obtained satisfactory values (q2 = 0.664, r2 = 0.917, SEE = 0.217) while optimized CoMSIA model exceed with (q2 = 0.672, r2 = 0.948, SEE = 0.175). Conclusion: The statistical parameters from 3D-QSAR models reveal that the results are reliable and significant with strong predictive ability. These theoretical results may contribute to the design of novel HDAC small molecule inhibitors with enhanced activity for the treatment of cancer.

“2D, 3D QSAR and Pharmacophore Identification of Thieno[3,2-d]pyrimidines as Cholesterol inhibitors.”

Introduction:: The present study reveals the 2D, 3D-QSAR analysis of Thieno[3,2-d]pyrimidine to express the biological activity against Cholesterol, structurally different ligands can fit the common receptor site, and Pharmacophore models well describe safety considerations of the said chemical entities Methods:: The organic exercises of the atoms were changed over into log IC50. The measurably huge of 2D-QSAR and 3D QSAR models are r2 = 0 .9762, q2 = 0.9379 and internal (q2 = 0.8837) and external (predictive r2 = 0.9162) respectively. 2D QSAR studies revealed that the Positive coefficient value of Quadrupole2 and Negative coefficient value of T_2_Cl_7 descriptors were major contributing descriptors. Results:: The 3D QSAR models indicate that steric and electrostatic effects primarily determine binding affinities. Conclusion:: From the best model obtained from the QSAR analyses, some newer compounds of the same series were developed, having better activity than the earlier compounds that have been reported.

Computational assessment and in vitro test of phytochemicals of Usnea aciculifera as potential inhibitors of Escherichia coli efflux pump AcrB

Lichens produce secondary metabolites that have many pharmaceutical activities such as antimicrobial, antioxidant, antiviral, anticancer, antigenotoxic, anti-inflammatory, analgesic and antipyretic activities. However, there is limited research on their efflux pump inhibitory activities. Twelve phytochemicals were isolated from Usnea aciculifera, and their activity of AcrAB-TolC efflux pump inhibition was evaluated. Four potential compounds, which are diffractaic acid (2), 8′ -O- methylstictic acid (5), 3-hydroxy-4-(methoxycarbonyl)-2,5-dimethylphenyl 2,4-dimethoxy-3,6-dimethylbenzoate (8) and 3-hydroxy-4-(methoxycarbonyl)-2,5-dimethylphenyl 2-hydroxy-4-methoxy-3,6-dimethylbenzoate (9), were found by virtual screening using pharmacophore and 2D-QSAR model. Compound 8 exhibited AcrB inhibition activity in vitro with an accumulation H33342 percentage compared with untreated control of 202% at a concentration of 50 µM and increased the antibacterial activity of levofloxacin by four-fold at a concentration of 200 µM. By molecular docking and molecular dynamics (MD) simulation, the binding affinity of depside and depsidone derivatives to AcrB was also clarified. Despite the poor docking score to the AcrB binding site, compound 8 was the most stable among the four complexes at 20 ns of MD simulation. The analysis of long MD at 100 ns indicated that compound 8 interacts strongly with the residues in the distal pocket, creating a stable complex with ΔGbind of −31.51 kcal.mol-1. According to the ADMETlab 2.0 web server’s predictions of pharmacokinetics and toxicities, compound 8 has the potential for drug development. Communicated by Ramaswamy H. Sarma

3D-QSAR and ADMET studies of morpholino-pyrimidine inhibitors of DprE1 from Mycobacterium tuberculosis

DprE1 is involved in the synthesis of Mycobacterium tuberculosis cell wall and is a potent drug target for Tuberculosis (TB) treatment. The structure and dynamics of the loops L-I and L-II flanking the inhibitor binding site was studied using molecular dynamics (MD) simulation and MMPBSA in Amber v18. Docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) of 55 Morpholino-pyrimidine (MP) inhibitors was carried out using Autodock v1.2.0 and Forge v10. ADMET analysis was done using SwissADME and pkCSM. All MP inhibitors docked in the DprE1 binding pocket, making contacts with L-II residues. MD studies showed that L-I and L-II unfold in the absence of the inhibitor but fold stably structure with reduced protein motions in the presence of MP-38, the highest affinity inhibitor. This was confirmed by k-means clustering and secondary structure analysis. L-II residues, L317, F320 and R325 contributed most towards the MMPBSA binding free energy of MP-38. A robust field-based 3D-QSAR model showed values of r2 train = 0.982, r2 test = 0.702 and q2 = 0.516. The MP inhibitor field points were broadly divided into negative electrostatics near the A, B rings and hydrophobic electrostatics near the D, E rings. Addition of negative groups at methanone position and ring B as well as addition of hydrophobic and bulky groups at ring E will improve activity. Highly active compounds 47, 49 and 50 of MP series exhibited highly favourable drug-like properties. SAR and ADMET insights attained from this model will help in the development of active DprE1 inhibitors in future. Communicated by Ramaswamy H. Sarma