Comparative QSAR:  Toward a Deeper Understanding of Chemicobiological Interactions

Hansch, Corwin; Hoekman, David; Gao, Hua

doi:10.1021/cr9400976

Cited by 225 publications

(155 citation statements)

References 149 publications

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“…In recent years, three-dimensional quantitative structure-activity relationship (3D-QSAR) study, which is a kind of statistical method combined with 3D structural information, physicochemical properties and activity relationship of the molecular, has a widely application in the development of various types of drugs [18][19][20] . For nearly a decade, the QSAR and molecular docking study of PI3K/Akt/mTOR pathway small molecule inhibitors have made great progress in drugs development, many novel mTOR inhibitors were used in clinical treatment [21,22] .…”

Section: Research Papermentioning

confidence: 99%

Combined 3D-QSAR and Molecular Docking Study on benzo[h][1,6]naphthyridin-2(1H)-one Analogs as mTOR Inhibitors

Wang¹,

Liu²,

Wang³

et al. 2018

pharmaceutical-sciences

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Wang, et al.: Studies of mTOR Inhibitors based on 3D-QSAR and Molecular DockingMechanistic target of rapamycin is involved in the formation of tumor microvasculature was an ideal target for computer-aided drug design. The predictive study of quantitative structure-activity relationship and molecular docking can shorten the cycle and reduce the cost of designing the higher activity mTOR inhibitors. In this article, comparative molecular field analysis and comparative molecular similarity indices analysis fields were used to analyse three-dimensional quantitative structure-activity relationship model. The model (comparative molecular similarity indices analysis with q 2 =0.607, r 2 =0.909; comparative molecular similarity indices analysis with q 2 =0.703, r 2 =0.935) has a good predictability. Three-dimensional quantitative structure-activity relationship model contour maps indicate the electrostatic, hydrophobic and hydrogen bond donor fields have crucial effects to derivatives biological activity. Molecular docking was employed to explore the conformations of 55 compounds with key amino acid residues. Finally, combining contour maps with molecular docking results, ten derivatives as potential mechanistic target of rapamycin inhibitors were designed to further verify established three-dimensional quantitative structure-activity relationship models. These data provide significant theoretical foundation for designing better activity mechanistic target of rapamycin inhibitors.

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Section: Research Papermentioning

confidence: 99%

Combined 3D-QSAR and Molecular Docking Study on benzo[h][1,6]naphthyridin-2(1H)-one Analogs as mTOR Inhibitors

Wang¹,

Liu²,

Wang³

et al. 2018

pharmaceutical-sciences

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show abstract

“…This is due to the extremely strong and complicated electrostatic and hydrogenbonding interactions (Estrada et al, 2007). The best developed classical method is the multivariate quantitative structureactivity/property relationship (QSAR/QSPR) methodology (Katritzky at al., 1997;Karelson et al, 1996;Hansch at al., 1996;Hansch, 1993, Cramer at al., 1988Klebe at al., 1994). The underlying assumption in this methodology is that the molecular structure of an organic compound contains, in principle, coded within it all of the information which predetermines the chemical, biological and physical properties of that compound.…”

Section: Molecular Approaches To Studying Chemistry In Solutionmentioning

confidence: 99%

Pyrimidinylsalicylic Based Herbicides: Modeling and Prediction

Delgado¹

2011

Herbicides and Environment

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“…One of the methods that have shown greatest promise is the use of quantitative structural-activity relationships (QSAR). [3][4][5][6][7] QSAR have provided a valuable tool in research on the toxicity of organic chemicals, which is based on the premise that the toxicity of a chemical is related to the physico-chemical structure; thus, toxicity can be related to the ability of a compound to reach the site of action, and its ability to react covalently there. 8 Such phenomena can be modeled adequately using physico-chemical parameters, such as hydrophobicity and electrophilicity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Acute in vivo Toxicity of Some Amine and Amide Drugs to Rats by Multiple Linear Regression, Partial Least Squares and an Artificial Neural Network

et al. 2007

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The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.

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Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions

Cited by 225 publications

References 149 publications

Combined 3D-QSAR and Molecular Docking Study on benzo[h][1,6]naphthyridin-2(1H)-one Analogs as mTOR Inhibitors

Combined 3D-QSAR and Molecular Docking Study on benzo[h][1,6]naphthyridin-2(1H)-one Analogs as mTOR Inhibitors

Pyrimidinylsalicylic Based Herbicides: Modeling and Prediction

Prediction of Acute in vivo Toxicity of Some Amine and Amide Drugs to Rats by Multiple Linear Regression, Partial Least Squares and an Artificial Neural Network

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