Displaying publications 21 - 40 of 59 in total

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  1. Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches
    Khan A, Shahab M, Nasir F, Waheed Y, Alshammari A, Mohammad A, et al.
    SAR QSAR Environ Res, 2023;34(9):689-708.
    PMID: 37675795 DOI: 10.1080/1062936X.2023.2250723
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  2. Synthesis of 2-(2-oxo-2H-chromen-4-yl)acetamides as potent acetylcholinesterase inhibitors and molecular insights into binding interactions
    Kara J, Suwanhom P, Wattanapiromsakul C, Nualnoi T, Puripattanavong J, Khongkow P, et al.
    Arch Pharm (Weinheim), 2019 Jul;352(7):e1800310.
    PMID: 31125474 DOI: 10.1002/ardp.201800310
    Sixteen novel coumarin-based compounds are reported as potent acetylcholinesterase (AChE) inhibitors. The most active compound in this series, 5a (IC50 0.04 ± 0.01 µM), noncompetitively inhibited AChE with a higher potency than tacrine and galantamine. Compounds 5d, 5j, and 5 m showed a moderate antilipid peroxidation activity. The compounds showed cytotoxicity in the same range as the standard drugs in HEK-293 cells. Molecular docking demonstrated that 5a acted as a dual binding site inhibitor. The coumarin moiety occupied the peripheral anionic site and showed π-π interaction with Trp278. The tertiary amino group displayed significant cation-π interaction with Phe329. The aromatic group showed π-π interaction with Trp83 at the catalytic anionic site. The long chain of methylene lay along the gorge interacting with Phe330 via hydrophobic interaction. Molecular docking was applied to postulate the selectivity toward AChE of 5a in comparison with donepezil and tacrine. Structural insights into the selectivity of the coumarin derivatives toward huAChE were explored by molecular docking and 3D QSAR and molecular dynamics simulation for 20 ns. ADMET analysis suggested that the 2-(2-oxo-2H-chromen-4-yl)acetamides showed a good pharmacokinetic profile and no hepatotoxicity. These coumarin derivatives showed high potential for further development as anti-Alzheimer agents.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  3. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain
    Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, et al.
    J Biomol Struct Dyn, 2024 Mar;42(5):2550-2569.
    PMID: 37144753 DOI: 10.1080/07391102.2023.2205948
    Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  4. Synthesis, anti-diabetic and in silico QSAR analysis of flavone hydrazide Schiff base derivatives
    Jamil W, Shaikh J, Yousuf M, Taha M, Khan KM, Shah SAA
    J Biomol Struct Dyn, 2022;40(23):12723-12738.
    PMID: 34514955 DOI: 10.1080/07391102.2021.1975565
    This study reports synthesis of flavone hydrazide Schiff base derivatives with diverse functionalities for the cure of diabetic mellitus and their a-glucosidase inhibitor and in silico studies. In this regard, Flavone derivatives 1-20 has synthesized and characterized by various spectroscopic techniques. These compounds showed significant potential towards a-glucosidase enzyme inhibition activity and found to be many fold better active than the standard Acarbose (IC50 = 39.45 ± 0.11 µM). The IC50values ranges 1.02-38.1 µM. Among these, compounds 1(IC50 = 4.6 ± 0.23 µM), 2(IC50 = 1.02 ± 0.2 µM), 3(IC50 = 7.1 ± 0.11 µM), 4(IC50 = 8.3 ± 0.34 µM), 5(IC50 = 7.4 ± 0.15 µM), 6(IC50 = 8.5 ± 0.27 µM) and 18 (IC50 = 1.09 ± 0.26 µM) showed highest activity. It was revealed that the analogues having -OH substitution have higher activity than their look likes. The molecular docking analysis revealed that these molecules have high potential to interact with the protein molecule and have high ability to bind with the enzyme. Furthermore, in silico pharmacokinetics, physicochemical studies were also performed for these derivatives. The bioavailability radar analysis explored that of all these compounds have excellent bioavailability for five (5) descriptors, however, the sixth descriptor of instauration is slightly increased in all compounds.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  5. Development of a novel mathematical model using a group contribution method for prediction of ionic liquid toxicities
    Ismail Hossain M, Samir BB, El-Harbawi M, Masri AN, Abdul Mutalib MI, Hefter G, et al.
    Chemosphere, 2011 Oct;85(6):990-4.
    PMID: 21794892 DOI: 10.1016/j.chemosphere.2011.06.088
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  6. Fulltext A Quantum Chemical and Statistical Study of Cytotoxic Activity of Compounds Isolated from Curcuma zedoaria
    Hamdi OA, Anouar el H, Shilpi JA, Trabolsy ZB, Zain SB, Zakaria NS, et al.
    Int J Mol Sci, 2015 Apr 27;16(5):9450-68.
    PMID: 25923077 DOI: 10.3390/ijms16059450
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  7. Effect of imidazolium-based ionic liquids on bacterial growth inhibition investigated via experimental and QSAR modelling studies
    Ghanem OB, Mutalib MI, El-Harbawi M, Gonfa G, Kait CF, Alitheen NB, et al.
    J Hazard Mater, 2015 Oct 30;297:198-206.
    PMID: 25965417 DOI: 10.1016/j.jhazmat.2015.04.082
    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%.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  8. Development of QSAR model to predict the ecotoxicity of Vibrio fischeri using COSMO-RS descriptors
    Ghanem OB, Mutalib MIA, Lévêque JM, El-Harbawi M
    Chemosphere, 2017 Mar;170:242-250.
    PMID: 28006757 DOI: 10.1016/j.chemosphere.2016.12.003
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  9. Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model
    Ghanem OB, Shah SN, Lévêque JM, Mutalib MIA, El-Harbawi M, Khan AS, et al.
    Chemosphere, 2018 Mar;195:21-28.
    PMID: 29248749 DOI: 10.1016/j.chemosphere.2017.12.018
    Over the past decades, Ionic liquids (ILs) have gained considerable attention from the scientific community in reason of their versatility and performance in many fields. However, they nowadays remain mainly for laboratory scale use. The main barrier hampering their use in a larger scale is their questionable ecological toxicity. This study investigated the effect of hydrophobic and hydrophilic cyclic cation-based ILs against four pathogenic bacteria that infect humans. For that, cations, either of aromatic character (imidazolium or pyridinium) or of non-aromatic nature, (pyrrolidinium or piperidinium), were selected with different alkyl chain lengths and combined with both hydrophilic and hydrophobic anionic moieties. The results clearly demonstrated that introducing of hydrophobic anion namely bis((trifluoromethyl)sulfonyl)amide, [NTF2] and the elongation of the cations substitutions dramatically affect ILs toxicity behaviour. The established toxicity data [50% effective concentration (EC50)] along with similar endpoint collected from previous work against Aeromonas hydrophila were combined to developed quantitative structure-activity relationship (QSAR) model for toxicity prediction. The model was developed and validated in the light of Organization for Economic Co-operation and Development (OECD) guidelines strategy, producing good correlation coefficient R2 of 0.904 and small mean square error (MSE) of 0.095. The reliability of the QSAR model was further determined using k-fold cross validation.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  10. QSAR and Pharmacophore Mapping Studies on Benzothiazinimines to Relate their Structural Features with anti-HIV Activity
    Geethaavacini G, Poh GP, Yan LY, Deepashini R, Shalini S, Harish R, et al.
    Med Chem, 2018;14(7):733-740.
    PMID: 29807521 DOI: 10.2174/1573406414666180529091618
    BACKGROUND: The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in a greatly extensive reduction in these drugs efficacy.

    OBJECTIVES: To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies.

    METHODS: QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance.

    RESULTS: The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors.

    CONCLUSION: The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.

    Matched MeSH terms: Quantitative Structure-Activity Relationship
  11. Fulltext Validation of quantitative structure-activity relationship (QSAR) model for photosensitizer activity prediction
    Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA
    Int J Mol Sci, 2011;12(12):8626-44.
    PMID: 22272096 DOI: 10.3390/ijms12128626
    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  12. Fulltext Highly accurate prediction of flammability limits of chemical compounds using novel integrated hybrid models
    El-Harbawi M, Samir BB, El Blidi L, Ben Ghanem O
    PLoS One, 2019;14(11):e0224807.
    PMID: 31725738 DOI: 10.1371/journal.pone.0224807
    Two novel and highly accurate hybrid models were developed for the prediction of the flammability limits (lower flammability limit (LFL) and upper flammability limit (UFL)) of pure compounds using a quantitative structure-property relationship approach. The two models were developed using a dataset obtained from the DIPPR Project 801 database, which comprises 1057 and 515 literature data for the LFL and UFL, respectively. Multiple linear regression (MLR), logarithmic, and polynomial models were used to develop the models according to an algorithm and code written using the MATLAB software. The results indicated that the proposed models were capable of predicting LFL and UFL values with accuracies that were among the best (i.e. most optimised) reported in the literature (LFL: R2 = 99.72%, with an average absolute relative deviation (AARD) of 0.8%; UFL: R2 = 99.64%, with an AARD of 1.41%). These hybrid models are unique in that they were developed using a modified mathematical technique combined three conventional methods. These models afford good practicability and can be used as cost-effective alternatives to experimental measurements of LFL and UFL values for a wide range of pure compounds.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  13. Fulltext Antioxidant activity of hispidin oligomers from medicinal fungi: a DFT study
    El Hassane A, Shah SA, Hassan NB, El Moussaoui N, Ahmad R, Zulkefeli M, et al.
    Molecules, 2014;19(3):3489-507.
    PMID: 24662069 DOI: 10.3390/molecules19033489
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  14. Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey
    Edros R, Feng TW, Dong RH
    SAR QSAR Environ Res, 2023;34(6):475-500.
    PMID: 37409842 DOI: 10.1080/1062936X.2023.2230868
    Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood-brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  15. Prediction of Anti-Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico
    Das S, Laskar MA, Sarker SD, Choudhury MD, Choudhury PR, Mitra A, et al.
    Phytochem Anal, 2017 Jul;28(4):324-331.
    PMID: 28168765 DOI: 10.1002/pca.2679
    INTRODUCTION: Prenylated and pyrano-flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti-cholinergic, anti-inflammatory, anti-microbial, anti-oxidant, anti-proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.

    OBJECTIVE: The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano-flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.

    METHODOLOGY: Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure-activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.

    RESULTS: In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as -13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. -31.6392 and IC50 was predicted as 6.025 nM.

    CONCLUSION: Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.

    Matched MeSH terms: Quantitative Structure-Activity Relationship
  16. Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ
    Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X
    J Chem Inf Model, 2023 Nov 13;63(21):6912-6924.
    PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252
    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.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  17. TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers
    Cao H, Ng MCK, Jusoh SA, Tai HK, Siu SWI
    J Comput Aided Mol Des, 2017 Sep;31(9):855-865.
    PMID: 28864946 DOI: 10.1007/s10822-017-0047-0
    [Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  18. Synthesis of α, β-unsaturated carbonyl based compounds as acetylcholinesterase and butyrylcholinesterase inhibitors: characterization, molecular modeling, QSAR studies and effect against amyloid β-induced cytotoxicity
    Bukhari SN, Jantan I, Masand VH, Mahajan DT, Sher M, Naeem-ul-Hassan M, et al.
    Eur J Med Chem, 2014 Aug 18;83:355-65.
    PMID: 24980117 DOI: 10.1016/j.ejmech.2014.06.034
    A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid β-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 μM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aβ-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  19. In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores
    Brahmachari G, Choo C, Ambure P, Roy K
    Bioorg Med Chem, 2015 Aug 01;23(15):4567-4575.
    PMID: 26105711 DOI: 10.1016/j.bmc.2015.06.005
    A series of densely functionalized piperidine (FP) scaffolds was synthesized following a diastereoselective one-pot multicomponent protocol under eco-friendly conditions. The FPs were evaluated in vitro for their acetylcholinesterase (AChE) inhibitory activity, and in silico studies for all the target compounds were carried out using pharmacophore mapping, molecular docking and quantitative structure-activity relationship (QSAR) analysis in order to understand the structural features required for interaction with the AChE enzyme and the key active site residues involved in the intermolecular interactions. Halogenation, nitration or 3,4-methylenedixoxy-substitution at the phenyl ring attached to the 2- and 6-positions of 1,2,5,6-tetrahydropyridine nucleus in compounds 14-17, 19, 20, 24 and 26 greatly enhanced the AChE inhibitory activity. The docking analysis demonstrated that the inhibitors are well-fitted in the active sites. The in silico studies enlighten the future course of studies in modifying the scaffolds for better therapeutic efficacy against the deadly Alzheimer's disease.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  20. QSPR prediction of the hydroxyl radical rate constant of water contaminants
    Borhani TN, Saniedanesh M, Bagheri M, Lim JS
    Water Res, 2016 07 01;98:344-53.
    PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038
    In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
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