Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening

Designing of thiazolidinones against chicken pox, monkey pox, and hepatitis viruses: A computational approach

Computational designing of four different series (D-G) of thiazolidinone was done starting from different amines which was further condensed with various aldehydes. These underwent in silico molecular investigations for density functional theory (DFT), molecular docking, and absorption, distribution metabolism, excretion, and toxicity (ADMET) studies. The different electrochemical parameters of the compounds are predicted using quantum mechanical modeling approach with Gaussian. The docking software was used to dock the compounds against choosing PDB file for chickenpox, human immunodeficiency, hepatitis, and monkeypox virus as 1OSN, 1VZV, 6VLK, 1RTD, 3I7H, 3TYV, 4JU3, and 4QWO, respectively. The molecular interactions were visualized with discovery studio and maximum binding affinity was observed with D8 compounds against 4QWO (-13.383 kcal/mol) while for compound D5 against 1VZV which was -12.713 kcal/mol. Swiss ADME web tool was used to assess the drug-likeness of the designed compounds under consideration, and it is concluded that these molecules had a drug-like structure with almost zero violations.

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.

Pharmacological Mechanism of Zuojin Pill for Gastroesophageal Reflux Disease: A Network Pharmacology Study

Background. Although Zuojin Pill (ZJP) is widely used in China as a traditional prescription to treat gastroesophageal reflux disease (GERD), its exact mechanism of action is still unknown. Therefore, we employed network pharmacology (NP), molecular docking (MD), and molecular dynamics simulation (MDS) to investigate the pharmacological mechanisms of ZJP against GERD. Methods. Active compounds and target genes corresponding to ZJP and target genes related to GERD were identified through analysis of publicly available datasets. Subsequently, the obtained data were subjected to further network pharmacological analysis to explore the potential key active compounds, core target genes, and biological processes (BPs) associated with the effect of ZJP against GERD. Finally, the prediction results of NP were validated by MD, and MDS of the optimal core protein-ligand for each component obtained by MD were performed using Gromacs 2020 software. Results. Twelve active components of ZJP were identified to act on 82 target genes associated with GERD, and ZJP might exert an anti-GERD effect through the regulation of BPs such as reactive oxygen species (ROS) metabolism, response to oxidative stress (OS), and ROS, as well as the activation of signaling pathways such as apoptosis, p53 signaling, chemical carcinogenesis-ROS, and HIF-1 signaling pathways. Furthermore, quercetin, kaempferol, and coptisine, the three key components of ZJP were shown to stably bond with the 14 core target genes, including AKT1, MMP2, TP53, EGFR, JUN, CASP3, CXCL8, HIF1α, IL-1β, MYC, PPARG, MMP9, PTGS2, and FOS. Results from MDS showed that PPARG-quercetin and MMP2-quercetin bound more stably. Conclusions. The findings suggest that ZJP alleviates the symptoms of GERD and improves the prognosis by regulating ROS metabolism, thereby reducing the secretion of proinflammatory cytokines like IL-1β, COX-2, CXCL8, and MMPs, regulating the expression of oncogenes such as JUN and FOS, and maintaining the normal expression of tumor suppressor genes such as TP53 and MYC. However, whether the effect of this modulation of ROS metabolism is positive or negative needs to be further verified by pharmacological experiments.

Molecular Mechanism of the Effect of Zhizhu Pill on Gastroesophageal Reflux Disease Based on Network Pharmacology and Molecular Docking

Background

To investigate the pharmacological mechanism of Zhizhu pill (ZZP) against gastroesophageal reflux disease (GERD), network pharmacology in combination with molecular docking was applied in this study.

Methods

Active compounds of ZZP and target genes related to GERD were identified through public databases. Subsequently, the obtained data were used as a basis for further network pharmacological analysis to explore the potential key active compounds, core targets, and biological processes involved in ZZP against GERD. Finally, the results predicted by network pharmacology were validated by molecular docking.

Results

Twenty active components of ZZP were identified to act on 59 targets related to GERD. Enrichment analysis revealed that multiple biological processes including response to oxygen levels, response to oxidative stress, and response to reactive oxygen species were involved in the GERD ZZP treatment with ZZP. ZZP had an impact on the prognosis of GERD mainly through the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and pathways in cancer. Further analysis identified the key components and core targets of ZZP against GERD, of which nobiletin, didymin, luteolin, and naringenin were key components, and PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA were the core targets. Molecular docking verified the stable bonds formed between the key components and the core targets.

Conclusions

The results of this study predict that the therapeutic effects of ZZP in GERD are mediated at least in part via PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA. These results may be useful in providing an experimental basis and new ideas for further research on ZZP in GERD.

Exploration of the Potential Mechanisms of Wumei Pill for the Treatment of Ulcerative Colitis by Network Pharmacology

Background

Wumei pill (WMP) has a long history of colitis treatment in China, but the protective mechanisms have not been elucidated. To uncover the potential mechanisms of WMP against ulcerative colitis (UC), the network pharmacology approach was utilized in this study.

Methods

Public databases were utilized to identify the potential targets of WMP and genes related to UC. Based on the identified overlapping common targets, drug-ingredient-target gene network, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) analysis were conducted. Molecular docking was carried out to verify the selected key active ingredients and core targets.

Results

129 active ingredients and 622 target genes were obtained. The drug-ingredient-target gene network revealed 52 active ingredients of WMP acting on 73 targets related to UC. GO analysis revealed that biological processes were mainly associated with oxidative stress, such as, reactive oxygen species metabolic processes, response to oxidative stress, cellular response to oxidative stress, response to reactive oxygen species, and regulation of reactive oxygen species metabolic processes. KEGG analysis revealed that the immune- and inflammation-related pathways, tumor-related signaling pathways, and microbial infection-related signaling pathways were the most significant. PPI network identified 13 core target genes. The molecular docking results indicated the formation of stable bonds between the active ingredients and core target genes.

Conclusions

The approach of network pharmacology reveals the key ingredients, potential core targets, and biological process of WMP in the treatment of UC. The mechanisms of action of WMP involve anti-inflammation, antioxidation, and modulation of immunity, which provides evidence for the therapeutic role of WMP in UC.

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach

Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6–1.2 mmol/L or 0.0–0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70–0.73 and 0.68–0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6–1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67–0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring.

Medicinal chemistry of anthranilic acid derivatives: A mini review

Anthranilic acid and its analogues present a privileged profile as pharmacophores for the rational development of pharmaceuticals deliberated for managing the pathophysiology and pathogenesis of various diseases. The substitution on anthranilic acid scaffold provides large compound libraries, which enable a comprehensive assessment of the structure activity relationship (SAR) analysis for the identification of hits and leads in a typical drug development paradigm. Besides, their widespread applications as anti-inflammatory fenamates, the amide and anilide derivatives of anthranilic acid analogues play a central role in the management of several metabolic disorders. In addition, these derivatives of anthranilic acid exhibit interesting antimicrobial, antiviral and insecticidal properties, whereas the derivatives based on anthranilic diamide scaffold present applications as P-glycoprotein inhibitors for managing the drug resistance in cancer cells. In addition, the anthranilic acid derivatives serve as the inducers of apoptosis, inhibitors of hedgehog signaling pathway, inhibitors of mitogen activated protein kinase pathway, and the inhibitors of aldo-keto reductase enzymes. The antiviral derivatives of anthranilic acid focus on the inhibition of hepatitis C virus NS5B polymerase to manifest considerable antiviral properties. The anthranilic acid derivatives reportedly present neuroprotective applications by downregulating the key pathways responsible for the manifestation of neuropathological features and neurodegeneration. Nevertheless, the transition metal complexes of anthranilic acid derivatives offer therapeutic applications in diabetes mellitus, and obesity by regulating the activity of α-glucosidase. The present review demonstrates a critical analysis of the therapeutic profile of the key derivatives of anthranilic acid and its analogues for the rational development of pharmaceuticals and therapeutic molecules.

Predicting aqueous solubility by QSPR modeling

The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R² greater than 0.7 and Q² greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R² and standard deviation.

Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Buyang Huanwu Decoction in the Treatment of Ischemic Stroke

Methods

The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SwissTargetPrediction, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-target network, and herb-target-pathway network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification.

Results

A total of 235 putative targets were obtained from 59 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved in TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-sitosterol, kaempferol, stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets.

Conclusions

Our study demonstrated that BHD exerted the effect of treating IS by regulating multitargets and multichannels with multicomponents through the method of network pharmacology and molecular docking.

Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations

The umbrella sampling (US) approach has been demonstrated to be a very efficient method for estimating the ligand-binding affinity. However, most of the calculated values overestimate experimental ones that are probably caused by the inaccurate representation of the interaction between the ligand and the surrounding molecules. The issue can be resolved via the implementation aspects of λ-alteration simulation into the US approach, which we call the λ-dependent umbrella sampling (λUS) scheme. In particular, the electrostatic and van der Waals interactions were simultaneously changed by using the coupling parameter λ during λUS simulations. The mean value of obtained results, ∆ G US λ = 0.20 = - 11.59 ± 1.51 kcal mol^-1 , is in good fitting to the mean value of respective experiments, ∆G_EXP =  - 11.26 ± 0.89 kcal mol^-1 . Moreover, the correlation between the proposed approach and experiment is quite good with a value of R US λ = 0.20 = 0.82 ± 0.10 . The λUS scheme significantly enhances the calculated accuracy since the RMSE of the proposed scheme is smaller than traditional US simulations, RMSE US λ = 0.20 = 2.99 ± 0.82 kcal mol^-1 versus RMSE US λ = 0.00 = 5.48 ± 0.81 kcal mol^-1 . Furthermore, the precision is increased since the computed error via λUS approach, δ US λ = 0.20 = 1.51 kcal mol^-1 , was smaller than those of the US simulation, δ US λ = 0.00 = 1.78 kcal mol^-1 . Overall, the proposed approach perhaps provides an efficient way to accurately and precisely estimate the ligand-binding free energy.

Synthetic Analogues of Aminoadamantane as Influenza Viral Inhibitors-In Vitro, In Silico and QSAR Studies

A series of nineteen amino acid analogues of amantadine (Amt) and rimantadine (Rim) were synthesized and their antiviral activity was evaluated against influenza virus A (H3N2). Among these analogues, the conjugation of rimantadine with glycine illustrated high antiviral activity combined with low cytotoxicity. Moreover, this compound presented a profoundly high stability after in vitro incubation in human plasma for 24 h. Its thermal stability was established using differential and gravimetric thermal analysis. The crystal structure of glycyl-rimantadine revealed that it crystallizes in the orthorhombic Pbca space group. The structure–activity relationship for this class of compounds was established, with CoMFA (Comparative Molecular Field Analysis) 3D-Quantitative Structure Activity Relationships (3D-QSAR) studies predicting the activities of synthetic molecules. In addition, molecular docking studies were conducted, revealing the structural requirements for the activity of the synthetic molecules.

A diarylamine derived from anthranilic acid inhibits ZIKV replication

Zika virus (ZIKV) is a mosquito-transmitted Flavivirus, originally identified in Uganda in 1947 and recently associated with a large outbreak in South America. Despite extensive efforts there are currently no approved antiviral compounds for treatment of ZIKV infection. Here we describe the antiviral activity of diarylamines derived from anthranilic acid (FAMs) against ZIKV. A synthetic FAM (E3) demonstrated anti-ZIKV potential by reducing viral replication up to 86%. We analyzed the possible mechanisms of action of FAM E3 by evaluating the intercalation of this compound into the viral dsRNA and its interaction with the RNA polymerase of bacteriophage SP6. However, FAM E3 did not act by these mechanisms. In silico results predicted that FAM E3 might bind to the ZIKV NS3 helicase suggesting that this protein could be one possible target of this compound. To test this, the thermal stability and the ATPase activity of the ZIKV NS3 helicase domain (NS3Hel) were investigated in vitro and we demonstrated that FAM E3 could indeed bind to and stabilize NS3Hel.

Dibenzepinones, dibenzoxepines and benzosuberones based p38α MAP kinase inhibitors: Their pharmacophore modelling, 3D-QSAR and docking studies

In the present study, a series of dibenzepinones, dibenzoxepines, and benzosuberones targeting p38α MAP kinase were subjected to pharmacophore modelling, 3D-QSAR and molecular docking studies. The IC₅₀ values for these 67 compounds ranged between 0.003 and 6.80 μM. A five-point model (DDHHR.8) was generated using these compounds. This model was found to be statistically significant and was found to have high correlation (R² = 0.98), cross-validation coefficient (Q² = 0.95) and F (330) values at six component PLS factor. Tests were performed to ascertain the efficacy of the generated model. These tests included external validation, Tropsha's test for predictive ability, Y-randomisation test and domain of applicability (APD). In order to check the restrictivity of the model, enrichment studies were performed with inactive compounds by using decoy set molecules. To evaluate the effectiveness of the docking protocol, the co-crystallised ligand was extracted from the ligand-binding domain of the protein and was re-docked into the same position. Both the conformers were then superimposed, suggesting satisfactory docking parameters with an RMSD value of less than 1.0 Å (0.853 Å). A 10 ns molecular dynamics simulation confirmed the docking results of the 3UVP-ligand complex and the presumed active conformation. The outcome of the present study provides insight into the molecular features that promote bioactivity and can be exploited for the prediction of novel potent p38α MAP kinase inhibitors before carrying out their synthesis and anticancer evaluation.

In Silico Design of New B-Raf Kinase Type-II Inhibitors Through Combined Molecular Modeling Studies

Background:

B-Raf has become an important and exciting therapeutic cancer target.

Methods:

In the present work, molecular modeling protocols like molecular docking, MM/GBSA calculations, 3D-QSAR and binding site detection were performed on a dataset of 41 Type II inhibitors. Molecular docking was applied to explore the detailed binding process between the inhibitors and B-Raf kinase. Furthermore, the good linear relationships between G-Scores and MM/GBSA calculated and the experimental activity were shown. The satisfactory CoMFA and CoMSIA were constructed based on the conformations obtained by molecular docking.

Results:

The key structural requirements for increasing biological activity were verified by analyzing 3D contour maps of the 3D-QSAR models. FTMap and SiteMap were also used to detect the more efficient active binding site.

Conclusion:

New inhibitors were synthesized and the biological activities were evaluated, the results further validated our design strategy.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines

We have carried out high-level quantum chemical computations followed by molecular docking studies on a set of 17C₅-arylidene rhodanine isomers to provide insights into the binding modes with different reported binding pockets of the nonstructural protein 5B (NS5B) polymerase that contribute to the hepatitis C virus (HCV) inhibition. We optimized the multi-target profile of the selected rhodanine analogs to investigate potential non-nucleotide inhibitors (NNIs) by quantum chemical optimization of the 18 isomers followed by docking with quantum chemically optimized structures of each isomer with NS5B polymerase at multiple binding pockets. The binding affinities of the PP-I, PP-II and TP-II pockets of NS5B polymerase were analyzed for all the 17 isomers of 2-[(5Z)-5-(2,4-dichlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid. On the basis of binding propensity at the different pockets and inhibitor constants, we ranked these isomers as potential candidates for the HCV inhibition. We have identified four isomers as promising NNIs of NS5B polymerase with comparable binding and inhibition to the standard (1,3) dichloro substituted isomer that exhibits in vitro activity and several other isomers as candidates in a "multi-targeted drug" approach.

Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads

Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski's rule of five and its variants.

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.