Comparison of AM1 and B3LYP‐DFT for Inhibition of MAO‐A by Phenylisopropylamines: A QSAR Study

Deeb, Omar; Clare, Brian W.

doi:10.1111/j.1747-0285.2008.00643.x

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…Thus, the lowest energy non-redundant conformers of a ligand were used for pharmacophore model development. [16] A couple of conformer was defined as identical if the relative distance between them is below 1.00 Å. [17]…”

Section: Methodsmentioning

confidence: 99%

Pharmacophore modeling and 3D quantitative structure-activity relationship analysis of febrifugine analogues as potent antimalarial agent

Sen

Chatterjee

2013

J Adv Pharm Technol Res

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Febrifugine and its derivatives are effective against Plasmodium falciparum. Using PHASE algorithm, a five-point pharmacophore model with two hydrogen bond acceptor (A), one positively ionizable (P) and two aromatic rings (R), was developed to derive a predictive ligand-based statistically significant 3D-quantitative structure-activity relationship (QSAR) model (r2 = 0.972, SD = 0.3, F = 173.4, Q2 = 0.712, RMSE = 0.3, Person-R = 0.94, and r2pred = 0.8) to explicate the structural attributes crucial for antimalarial activity. The developed pharmacophore model and 3D QSAR model can be a substantial tool for virtual screening and related antimalarial drug discovery research.

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Section: Methodsmentioning

confidence: 99%

Pharmacophore modeling and 3D quantitative structure-activity relationship analysis of febrifugine analogues as potent antimalarial agent

Sen

Chatterjee

2013

J Adv Pharm Technol Res

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“…A previous study carried out in phenylalkylamines also revealed that electronic descriptors had an important contribution in the development of the QSAR model [83]. Deeb et al [101] further studied a set of phenylisopropylamines through semiempirical (AM1) and density functional theory (DFT) calculations [102]. They showed that the orientation of nodes and the energies of the occupied π orbitals have a powerful explanatory contribution to the variance in the MAO-A activity.…”

Section: Prediction Of Mao Activity Through 3d Ligand-based Modelsmentioning

confidence: 99%

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Vilar

Ferino²,

Quezada³

et al. 2012

Current Topics in Medicinal Chemistry

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The evolution of bio- and cheminformatics associated with the development of specialized software and increasing computer power has produced a great interest in theoretical in silico methods applied in drug rational design. These techniques apply the concept that “similar molecules have similar biological properties” that has been exploited in Medicinal Chemistry for years to design new molecules with desirable pharmacological profiles. Ligand-based methods are not dependent on receptor structural data and take into account two and three-dimensional molecular properties to assess similarity of new compounds in regards to the set of molecules with the biological property under study. Depending on the complexity of the calculation, there are different types of ligand-based methods, such as QSAR (Quantitative Structure-Activity Relationship) with 2D and 3D descriptors, CoMFA (Comparative Molecular Field Analysis) or pharmacophoric approaches. This work provides a description of a series of ligand-based models applied in the prediction of the inhibitory activity of monoamine oxidase (MAO) enzymes. The controlled regulation of the enzymes’ function through the use of MAO inhibitors is used as a treatment in many psychiatric and neurological disorders, such as depression, anxiety, Alzheimer’s and Parkinson’s disease. For this reason, multiple scaffolds, such as substituted coumarins, indolylmethylamine or pyridazine derivatives were synthesized and assayed toward MAO-A and MAO-B inhibition. Our intention is to focus on the description of ligand-based models to provide new insights in the relationship between the MAO inhibitory activity and the molecular structure of the different inhibitors, and further study enzyme selectivity and possible mechanisms of action.

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“…Full geometric optimizations were performed for all structures by the DFT method with the hybrid functional B3LYP (Becke's three‐parameter function using the Lee, Yang, and Parr correlation function)31, 32 at the 6‐311+G(d,p) theoretical level. Previous studies28, 29, 33–44 have demonstrated the DFT‐B3LYP method to be appropriate for the investigation of the structure‐property relationships of carcinogens and drugs. Because the biomolecular damage in vivo induced by CCNU derivatives took place in aqueous solution, the solvent effect of water was taken into account.…”

Section: Models and Computationsmentioning

confidence: 99%

Relationship between the molecular structure and the anticancer activity of N‐(2‐chloroethyl)‐N′‐cyclohexyl‐N‐nitrosoureas: A theoretical investigation

Cao

Zhao

Jin

et al. 2011

Int J of Quantum Chemistry

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N-(2-chloroethyl)-N 0 -cyclohexyl-N-nitrosoureas (CCNU) is an important alkylating agent used in the clinical treatment of cancer. The quantitative structureactivity relationship (QSAR) of CCNU derivatives was investigated using the density functional theory (DFT)-based descriptors and the n-octanol/water partition coefficient (milogP). Geometry optimization was performed using the DFT/B3LYP method in conjunction with the 6-311þG(d,p) basis set. Experimental data of anticancer activity, log(1/C), were used for the QSAR analysis. By stepwise multiple regressions, four optimum descriptors, (milog P)2 , E 1 , E Z/E and B O1ACl8 were found for constructing the QSAR models. Two satisfied models were obtained by multiple linear regressions with the values of R 2 higher than 0.9. The (milog P) 2 descriptor has the highest correlation to the anticancer activity, indicating that similar improvements to hydrophilicity and lipophilicity are necessary for enhancing anticancer activity. The energy barriers for the decomposition of CCNU derivatives via a retro-ene reaction (E 1 ) and for the transformation from Z-tautomers to E-tautomers (E Z/E ) are also considerable descriptors in the QSAR models. The anticancer activity is increased with the decrease of E 1 and the increase of E Z/E . The B O1ACl8 descriptor, which requires the inclusion of the other three descriptors in the models, has positive correlation with the anticancer activity of CCNU derivatives. The results indicate that the introduction of the descriptors of activation energies (E 1 and E Z/E ) is a significant contribution to the methodology of QSAR investigations, because the dynamic descriptors may be more correlated with the biological activity of drugs and toxicants than the static descriptors. Our models shed light on the structure-activity relationship of CCNU derivatives and may be useful in the development of more effective and less toxic nitrosoureas as anticancer agents.

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Comparison of AM1 and B3LYP‐DFT for Inhibition of MAO‐A by Phenylisopropylamines: A QSAR Study

Cited by 13 publications

References 22 publications

Pharmacophore modeling and 3D quantitative structure-activity relationship analysis of febrifugine analogues as potent antimalarial agent

Pharmacophore modeling and 3D quantitative structure-activity relationship analysis of febrifugine analogues as potent antimalarial agent

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Relationship between the molecular structure and the anticancer activity of N‐(2‐chloroethyl)‐N′‐cyclohexyl‐N‐nitrosoureas: A theoretical investigation

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