The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on
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compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.
A series of 21 compounds isolated from Curcuma zedoaria was subjected to cytotoxicity test against MCF7; Ca Ski; PC3 and HT-29 cancer cell lines; and a normal HUVEC cell line. To rationalize the structure-activity relationships of the isolated compounds; a set of electronic; steric and hydrophobic descriptors were calculated using density functional theory (DFT) method. Statistical analyses were carried out using simple and multiple linear regressions (SLR; MLR); principal component analysis (PCA); and hierarchical cluster analysis (HCA). SLR analyses showed that the cytotoxicity of the isolated compounds against a given cell line depend on certain descriptors; and the corresponding correlation coefficients (R2) vary from 0%-55%. MLR results revealed that the best models can be achieved with a limited number of specific descriptors applicable for compounds having a similar basic skeleton. Based on PCA; HCA and MLR analyses; active compounds were classified into subgroups; which was in agreement with the cell based cytotoxicity assay.
A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.
A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.
Hispidin oligomers are styrylpyrone pigments isolated from the medicinal fungi Inonotus xeranticus and Phellinus linteus. They exhibit diverse biological activities and strong free radical scavenging activity. To rationalize the antioxidant activity of a series of four hispidin oligomers and determine the favored mechanism involved in free radical scavenging, DFT calculations were carried out at the B3P86/6-31+G (d, p) level of theory in gas and solvent. The results showed that bond dissociation enthalpies of OH groups of hispidin oligomers (ArOH) and spin density delocalization of related radicals (ArO•) are the appropriate parameters to clarify the differences between the observed antioxidant activities for the four oligomers. The effect of the number of hydroxyl groups and presence of a catechol moiety conjugated to a double bond on the antioxidant activity were determined. Thermodynamic and kinetic studies showed that the PC-ET mechanism is the main mechanism involved in free radical scavenging. The spin density distribution over phenoxyl radicals allows a better understanding of the hispidin oligomers formation.
Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
Benzyl-o-vanillin and benzimidazole nucleus serve as important pharmacophore in drug discovery. The benzyl vanillin (2-(benzyloxy)-3-methoxybenzaldehyde) compound shows anti-proliferative activity in HL60 leukemia cancer cells and can effect cell cycle progression at G2/M phase. Its apoptosis activity was due to disruption of mitochondrial functioning. In this study, we have studied a series of compounds consisting of benzyl vanillin and benzimidazole structures. We hypothesize that by fusing these two structures we can produce compounds that have better anticancer activity with improved specificity particularly towards the leukemia cell line. Here we explored the anticancer activity of three compounds namely 2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2MP, N-1-(2-benzyloxy-3-methoxybenzyl)-2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2XP, and (R) and (S)-1-(2-benzyloxy-3-methoxyphenyl)-2, 2, 2-trichloroethyl benzenesulfonate, 3BS and compared their activity to 2-benzyloxy-3-methoxybenzaldehyde, (Bn1), the parent compound. 2XP and 3BS induces cell death of U937 leukemic cell line through DNA fragmentation that lead to the intrinsic caspase 9 activation. DNA binding study primarily by the equilibrium binding titration assay followed by the Viscosity study reveal the DNA binding through groove region with intrinsic binding constant 7.39 µM/bp and 6.86 µM/bp for 3BS and 2XP respectively. 2XP and 3BS showed strong DNA binding activity by the UV titration method with the computational drug modeling showed that both 2XP and 3BS failed to form any electrostatic linkages except via hydrophobic interaction through the minor groove region of the nucleic acid. The benzylvanillin alone (Bn1) has weak anticancer activity even after it was combined with the benzimidazole (2MP), but after addition of another benzylvanillin structure (2XP), stronger activity was observed. Also, the combination of benzylvanillin with benzenesulfonate (3BS) significantly improved the anticancer activity of Bn1. The present study provides a new insight of benzyl vanillin derivatives as potential anti-leukemic agent.
A linear quantitative structure activity relationship (QSAR) model is presented for predicting human immunodeficiency virus-1 (HIV-1) reverse transcriptase enzyme inhibition. The 2D QSAR and 3D-QSAR models were developed by stepwise multiple linear regression, partial least square (PLS) regression and k-nearest neighbor-molecular field analysis, PLS regression, respectively using a database consisting of 33 recently discovered benzoxazinones. The primary findings of this study is that the number of hydrogen atoms, number of (-NH2) group connected with solitary single bond alters the inhibition of HIV-1 reverse transcriptase. Further, presence of electrostatic, hydrophobic and steric field descriptors significantly affects the ability of benzoxazinone derivatives to inhibit HIV-1 reverse transcriptase. The selected descriptors could serve as a primer for the design of novel and potent antagonists of HIV-1 reverse transcriptase.
Glioblastoma multiforme is a highly malignant, heterogenic, and drug resistant tumor. The blood-brain barrier (BBB), systemic cytotoxicity, and limited specificity are the main obstacles in designing brain tumor drugs. In this study a computational approach was used to design brain tumor drugs that could downregulate VEGF and IL17A in glioblastoma multiforme type four. Computational screening tools were used to evaluate potential candidates for antiangiogenic activity, target binding, BBB permeability, and ADME physicochemical properties. Additionally, in vitro cytotoxicity, migration, invasion, tube formation, apoptosis, ROS and ELISA assays were conducted for molecule 6 that was deemed most likely to succeed. The efflux ratio of membrane permeability and calculated docking scores of permeability to glycoproteins (P-gps) were used to determine the BBB permeability of the molecules. The results showed BBB permeation for molecule 6, with the predicted efficiency of 0.55kcal/mol and binding affinity of -37kj/mol corresponding to an experimental efflux ratio of 0.625 and predicted -15kj/mol of binding affinity for P-gps. Molecule 6 significantly affected the angiogenesis pathways by 2-fold downregulation of IL17A and VEGF through inactivation of active sites of HSP90 (predicted binding: -37kj/mol, predicted efficiency: 0.55kcal/mol) and p23 (predicted binding: 12kj/mol, predicted efficiency: 0.17kcal/mol) chaperon proteins. Additionally, molecule 6 activated the 17.38% relative fold of ROS level at 18.3μg/mL and upregulated the caspase which lead the potential synergistic apoptosis through the antiangiogenic activity of molecule 6 and thereby the highly efficacious anticancer upshot. The results indicate that the binding of the molecules to the therapeutic target is not essential to produce a lethal effect on cancer cells of the brain and that antiangiogenic efficiency is much more important.
Ionic liquids (ILs) are class of solvent whose properties can be modified and tuned to meet industrial requirements. However, a high number of potentially available cations and anions leads to an even increasing members of newly-synthesized ionic liquids, adding to the complexity of understanding on their impact on aquatic organisms. Quantitative structure activity∖property relationship (QSAR∖QSPR) technique has been proven to be a useful method for toxicity prediction. In this work,σ-profile descriptors were used to build linear and non-linear QSAR models to predict the ecotoxicities of a wide variety of ILs towards bioluminescent bacterium Vibrio fischeri. Linear model was constructed using five descriptors resulting in high accuracy prediction of 0.906. The model performance and stability were ascertained using k-fold cross validation method. The selected descriptors set from the linear model was then used in multilayer perceptron (MLP) technique to develop the non-linear model, the accuracy of the model was further enhanced achieving high correlation coefficient with the lowest value being 0.961 with the highest mean square error of 0.157.
A new mathematical model has been developed that expresses the toxicities (EC₅₀ values) of a wide variety of ionic liquids (ILs) towards the freshwater flea Daphnia magna by means of a quantitative structure-activity relationship (QSAR). The data were analyzed using summed contributions from the cations, their alkyl substituents and anions. The model employed multiple linear regression analysis with polynomial model using the MATLAB software. The model predicted IL toxicities with R²=0.974 and standard error of estimate of 0.028. This model affords a practical, cost-effective and convenient alternative to experimental ecotoxicological assessment of many ILs.
Peroxisome Proliferator-Activated Receptor γ (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. With this in mind, we explored the pharmacophoric space of PPARγ using seven diverse sets of activators. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent and predictive quantitative structure-activity relationship (QSAR) (r2(71)=0.80, F=270.3, r2LOO=0.73, r2PRESS against 17 external test inhibitors=0.67). Three orthogonal pharmacophores emerged in the QSAR equation and were validated by receiver operating characteristic (ROC) curves analysis. The models were then used to screen the national cancer institute (NCI) list of compounds. The highest-ranking hits were tested in vitro. The most potent hits illustrated EC50 values of 15 and 224 nM.
The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q(2) of 0.516. The model has predicted r(2) of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.
Tuning the characteristics of solvents to fit industrial requirements has currently become a major interest in both academic and industrial communities, notably in the field of room temperature ionic liquids (RTILs), which are considered one of the most promising green alternatives to molecular organic solvents. In this work, several sets of imidazolium-based ionic liquids were synthesized, and their toxicities were assessed towards four human pathogens bacteria to investigate how tunability can affect this characteristic. Additionally, the toxicity of particular RTILs bearing an amino acid anion was introduced in this work. EC50 values (50% effective concentration) were established, and significant variations were observed; although all studied ILs displayed an imidazolium moiety, the toxicity values were found to vary between 0.05 mM for the most toxic to 85.57 mM for the least toxic. Linear quantitative structure activity relationship models were then developed using the charge density distribution (σ-profiles) as molecular descriptors, which can yield accuracies as high as 95%.
In the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreover, molecular simulation revealed stable dynamics, tighter structural packing and less flexible behaviour for all the complexes. We further reported that the average hydrogen bonds in TCM27763, TCM33057, TCM34450 and TCM31564I7L complexes remained higher than the control drug. Finally, the BF energy results revealed -62.60 ± 0.65 for the controlI7L complex, for the TCM27763I7L complex -71.92 ± 0.70 kcal/mol, for the TCM33057I7L complex the BF energy was -70.94 ± 0.70 kcal/mol, for the TCM34450I7L the BF energy was -69.94 ± 0.85 kcal/mol while for the TCM31564I7L complex the BF energy was calculated to be -69.16 ± 0.80 kcal/mol. Although, we used stateoftheart computational methods, these are theoretical insights that need further experimental validation.
Cancer is often associated with an aberrant increase in tubulin and microtubule activity required for cell migration, invasion, and metastasis. A new series of fatty acid conjugated chalcones have been designed as tubulin polymerization inhibitors and anticancer candidates. These conjugates were designed to harness the beneficial physicochemical properties, ease of synthesis, and tubulin inhibitory activity of two classes of natural components. New lipidated chalcones were synthesized from 4-aminoacetophenone via N-acylation followed by condensation with different aromatic aldehydes. All new compounds showed strong inhibition of tubulin polymerization and antiproliferative activity against breast and lung cancer cell lines (MCF-7 and A549) at low or sub-micromolar concentrations. A significant apoptotic effect was shown using a flow cytometry assay that corresponded to cytotoxicity against cancer cell lines, as indicated by a 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay. Decanoic acid conjugates were more potent than longer lipid analogues, with the most active being more potent than the reference tubulin inhibitor, combretastatin-A4 and the anticancer drug, doxorubicin. None of the newly synthesized compounds caused any detectable cytotoxicity against the normal cell line (Wi-38) or hemolysis of red blood cells below 100 μM. It is unlikely that the new conjugates described would affect normal cells or interrupt with cell membranes due to their lipidic nature. A quantitative structure-activity relationship analysis was performed to determine the influence of 315 descriptors of the physicochemical properties of the new conjugates on their tubulin inhibitory activity. The obtained model revealed a strong correlation between the tubulin inhibitory activity of the investigated compounds and their dipole moment and degree of reactivity.
Studies on green biosynthesis of newly engineered nanoparticles for their prominent medicinal applications are being the torch-bearing concerns of the state-of-the-art research strategies. In this concern, we have engineered the biosynthesized Luffa acutangula silver nanoparticles of flavonoid O-glycosides in the anisotropic form isolated from aqueous leave extracts of Luffa acutangula, a popular traditional and ayurvedic plant in south-east Asian countries. These were structurally confirmed by Ultraviolet-visible (UV-Vis), Fourier transform infrared spectroscopy accessed with attenuated total reflection (FTIR-ATR) spectral analyses followed by the scanning electron microscopic (SEM) and the X-ray diffraction (XRD) crystallographic studies and found them with the face-centered cubic (fcc) structure. Medicinally, we have explored their significant antioxidant (DPPH and ABTS assays), antibacterial (disc diffusion assay on E. coli, S. aureus, B. subtilis, S. fecilis, and S. boydii), and anticancer (MTT assay on MCF-7, MDA-MB-231, U87, and DBTRG cell lines) potentialities which augmented the present investigation. The molecular docking analysis of title compounds against 3NM8 (DPPH) and 1DNU (ABTS) proteins for antioxidant activity; 5FGK (Gram-Positive Bacteria) and 1AB4 (Gram-Negative Bacteria) proteins for antibacterial activity; and 4GBD (MCF-7), 5FI2 (MDA-MB-231), 1D5R (U87), and 5TIJ (DBTRG) proteins for anticancer activity has affirmed the promising ligand-protein binding interactions among the hydroxy groups of the title compounds and aspartic acid of the concerned enzymatic proteins. The binding energy varying from -9.1645 to -7.7955 for Cosmosioside (1, Apigenin-7-glucoside) and from -9.2690 to -7.8306 for Cynaroside (2, Luteolin-7-glucoside) implies the isolated compounds as potential bioactive compounds. In addition, the performed studies like QSAR, ADMET, bioactivity properties, drug scores, and toxicity risks confirmed them as potential drug candidates and aspartic acid receptor antagonists. This research auxiliary augmented the existing array of phytological nanomedicines with new drug candidates that are credible with multiple bioactivities.
A series of novel α-furfuryl-2-alkylaminophosphonates have been efficiently synthesized from the one-pot three-component classical Kabachnik-Fields reaction in a green chemical approach by addition of an in situ generated dialkylphosphite to Schiff's base of aldehydes and amines by using environmental and eco-friendly silica gel supported iodine as a catalyst by microwave irradiation. The advantage of this protocol is simplicity in experimental procedures and products were resulted in high isolated yields. The synthesized α-furfuryl-2-alkylaminophosphonates were screened to in vitro antioxidant and plant growth regulatory activities and some are found to be potent with antioxidant and plant growth regulatory activities. These in vitro studies have been further supported by ADMET (absorption, distribution, metabolism, excretion, and toxicity), quantitative structure-activity relationship, molecular docking, and bioactivity studies and identified that they were potentially bound to the GLN340 amino acid residue in chain C of 1DNU protein and TYR597 amino acid residue in chain A of 4M7E protein, causing potential exhibition of antioxidant and plant growth regulatory activities. Eventually, title compounds are identified as good blood-brain barrier (BBB)-penetrable compounds and are considered as proficient central nervous system active and neuroprotective antioxidant agents as the neuroprotective property is determined with BBB penetration thresholds.