QSAR studies of some side chain modified 7-chloro-4-aminoquinolines as antimalarial agents

Sahu, Nitendra K.; Sharma, Mukesh C.; Mourya, V. K.; Kohli, D. V.

doi:10.1016/j.arabjc.2010.12.005

Cited by 35 publications

(17 citation statements)

References 18 publications

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“…The first step consisted in obtaining the molecular geometry of all the derivatives from the dataset (Table 1) was energy minimization (Hansen, 2012) and geometry optimization using Merck Molecular Force Field (MMFF) in B3LYP/6-311 + G(d) density functional theoretical level was used to study QSAR (Sahu, Sharma, Mourya, & Kohli, 2010). …”

Section: Computational and Statistical Detailsmentioning

confidence: 99%

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

2016

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Quantitative structure-activity relationship (QSAR) is based on the hypothesis that changes in molecular structure reflect changes in the observed response or biological activity. The success of any QSAR model depends on the accuracy of the input data, selection of appropriate descriptors, statistical tools, and the validation of the developed model. A suitable set of molecular descriptors were calculated to represent the molecular structures of compounds such as constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. The important descriptors were selected with the aid of the genetic function approximation technique. The obtained model was validated using R 2 cv = 0.700, LOF = 0.187, R 2 = 0.8085, R 2 adj = 0.7625, F = 17.586, RMSE = 0.1781 and SDEP = 0.098, R 2 pred = 0.7956, L 5o = 0.7235. Results showed that the predictive ability of the model was satisfactory and it can be used for designing similar group of antifungal compounds.Abduljelil Ajala is currently an MSc student working under the supervision of Adamu Uzairu and Idris O Suleiman. Specialize on theoretical chemistry, working on modeling of organic compounds using different software and statistical tools. This research proposed a model which may provide a better understanding of the antifungal activity of ketone analogous and use as guidance for proposition of new chemopreventive agents. PUBLIC INTEREST STATEMENTQuantitative structure-activity relationship (QSAR) models are essential of the in silico techniques. They are utilized to anticipate the properties of chemicals, considering the potential harmfulness of chemicals in the body and the earth. QSAR model includes measurably examining the current information for a scope of chemicals so as to recognize precisely which structural, physical, and chemical properties of the molecule best correlate with particular measured effects on organism and the environment. QSAR models are available and in use for some end points. As a rule, QSAR models are more solid in the light of the fact that the marvel to be demonstrated is less mindboggling and a huge number of experimental results are accessible in the information bases. Though for models to foresee endless impacts, the information are considerably more restricted and the marvel is much perplexing. It likewise helps in dissecting crude test information and confirms the test results.

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Section: Computational and Statistical Detailsmentioning

confidence: 99%

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

2016

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“…For the present QSAR study, topological, shape and geometrical, electrostatic, physicochemical parameters such as lipophilicity (log P), Chi, ChiV, path count, ChiVChain, XlogP, smr, element count, estate number, estate contribution, semi-empirical, polar surface area, and alignment independent topological descriptors such as T_C_F_1, T_O_F_3, T_N_N_1) were used as predictor variables, as they were found to be appropriate for the development of models (Sahu et al, 2010). More than 480 molecular descriptors were calculated using Vlife MDS program.…”

Section: D Qsar Model Buildingmentioning

confidence: 99%

Structural insights into mode of actions of novel substituted 4- and 6-azaindole-3-carboxamides analogs as renin inhibitors: molecular modeling studies

Sharma

2014

Med Chem Res

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A series of substituted 4-and 6-azaindole-3-carboxamides derivatives displaying potent activities against renin inhibitors were selected to establish quantitative structure-activity relationships using two dimensional, k-nearest neighbor (kNN), and pharmacophore analysis methods. The QSAR models of the substituted 4-and 6-azaindole-3-carboxamides derivatives have been developed by partial least square (PLS) methodology coupled genetic algorithm, simulated annealing and stepwise, for the purpose of an effective comparison and some interesting results are obtained. The statistically best significant 2D-QSAR model having r 2 = 0.8644 and q 2 = 0.7630 with pred_r 2 = 0.8009 was developed by GA-PLS method with the descriptors like positive contribution of SsCH 3 E-index, T_C_F_5 T_2_Cl_1, SssOEindex. The result model of the QSAR study suggests the methyl, methoxy, fluorine, chlorine substituents in the nucleus will increase the binding affinity of 4-and 6-azaindole-3-carboxamides derivatives and positive contribution of descriptors illustrates that increase in branching and presence of methyl, methoxy, fluorine, chlorine is favorable for renin inhibitory activity. Further analysis using kNN methodology with GA-PLS, SA-PLS, and SW-PLS was used for building the QSAR models. The most significant model is having internal predictivity 74 % (q 2 = 0.7408) and external predictivity 78.3 % (pred_r 2 = 0.7837). Model showed that electrostatic (E_886, E_561) steric (S_522), and hydrophobic (H_1136) interactions play important role in determining renin inhibitory activity. The pharmacophore analysis of the molecules demonstrated that the AroC feature (aromatic), hydrogen-bond donor (HDr), hydrogen-bond acceptor (HAc), and PosC features are important pharmacophore contours favorable for these activities. The information obtained from 2D descriptors, three-dimensional contour maps and pharmacophore approach can be used for further design of substituted 4-and 6-azaindole-3-carboxamides analogs as renin inhibitors.

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“…In QSAR study, descriptor selection is a vital step. A fundamental characteristic of building any QSAR model is the selection of suitable set of parameters with good predictability [11,12] . So, the use of quantum chemical parameters has remarkable importance [13,14] .…”

Section: Introductionmentioning

confidence: 99%

Electronic descriptor based QSAR and Docking Studies of some 2-[5-(aryloxymethyl)-1, 3, 4-oxadiazol-2-ylsulfanyl] acetic acids as Anti-P. aeruginosa

Oyebamiji¹,

Mary²,

Benjamin³

et al. 2018

JPP

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Molecular parameters of 2-[5-(aryloxymethyl)-1, 3, 4-oxadiazol-2-ylsulfanyl] acetic acids and it derivatives were calculated using density Functional theory (DFT). Quantitative Structure Activity Relationship (QSAR) was developed for the antimicrobial activity of the compounds while the interaction between the compounds and the receptor (4r0s) was studied using docking method. We report the molecular electronic descriptors for anti-P.aeruginosa activities of the seven compounds. Using the developed QSAR models, the predicted bioactivity (IC 50) of the compounds fitted well with the experimentally observed IC 50. In addition, ligand-receptor interactions are reported and 2-(5-((4-nitrophenoxy) methyl)-1, 3, 4-oxadiazol-2-ylsulfanyl) acetic acid (A 7) showed the greatest affinity to bind on the active site of P. aeruginosa cell line

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QSAR studies of some side chain modified 7-chloro-4-aminoquinolines as antimalarial agents

Cited by 35 publications

References 18 publications

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

Structural insights into mode of actions of novel substituted 4- and 6-azaindole-3-carboxamides analogs as renin inhibitors: molecular modeling studies

Electronic descriptor based QSAR and Docking Studies of some 2-[5-(aryloxymethyl)-1, 3, 4-oxadiazol-2-ylsulfanyl] acetic acids as Anti-P. aeruginosa

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