QSAR of aromatic substances: MAO inhibitory activity of xanthone derivatives

Deeb, Omar; Alfalah, Sherin; Clare, Brian W.

doi:10.1080/14756360601161966

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In continuation to what was found in previous studies, e.g. (6,7,(12)(13)(14)(15)(16), the MAO-inhibitory activity of phenylisopropylamines expressed as pIC 50 is modeled in this study with the orientations of nodes in p-like orbitals of the phenyl ring and some other descriptors. The MAO-inhibitory activity of 46 phenylisopropylamines expressed as pIC 50 is summarized in Table 1.…”

mentioning

confidence: 89%

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

Deeb

Clare

2008

Chem Biol Drug Des

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The aim of this study is to provide an initial indication regarding the scope and limitations of some state-of-the-art methods in computational chemistry, including semiempirical (AM1) and density functional theory (B3LYP), in the flip regression procedure applied to the inhibition of phenylisopropylamines. The results show that the models established based on the density functional theory-B3LYP are better than that based on semiempirical method (AM1). It is demonstrated that electron-rich ring systems and highest occupied molecular orbital levels tended to increase activity. In this contribution, it is shown that the orientation of nodes in their occupied pi orbitals, and also the energies of these orbitals explain a further large portion of the variance in their inhibitory activity.

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confidence: 89%

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

Deeb

Clare

2008

Chem Biol Drug Des

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“…[16][17][18][19], all in vitro inhibitory activities against Candida albicans species are expressed as pMIC [M] = -log (MIC[M]), with the quantity MIC(M) representing the minimum inhibitory concentration in molar units, is modelled in this study with the descriptors mentioned above using Flip regression technique. All in vitro inhibitory activities of 96 heterocyclics expressed as pMIC are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

QSAR of heterocyclic antifungal agents by flip regression

Deeb

Clare

2008

J Comput Aided Mol Des

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QSAR analysis of a set of 96 heterocyclics with antifungal activity was performed. The results reveals that a pyridine ring is more favorable than benzene as the 6-membered ring, for high activity, but thiazole is unfavorable as the 5-membered ring relative to imidazole or oxazole. Methylene is the spacer leading to the highest activity. The descriptors used are indicator variables, which account for identity of substituent, lipophilicity and volume of substituent, and total polarizability. Unlike previously reported results for this data set, our fits do not exceed the limitations set by the nature of the data itself.

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“…Xanthone derivatives-Deeb et al [91] included information about nodal orientation to improve the xanthones MAO-A QSAR (see Fig 3, structure 1) previously published by Núñez et al [54]. They exploited the idea that orientations of the nodes in π-like orbitals of aromatic molecules are crucial in their activity [92,93].…”

Section: Qsar With 3d Descriptorsmentioning

confidence: 99%

“…The same dataset reported by Nuñez et al [54] and Deeb et al [91] including diverse xanthone derivatives was studied by Masand et al [94]. The structures of the compounds were optimized geometrically as a previous descriptors calculation step.…”

Section: Qsar With 3d Descriptorsmentioning

confidence: 99%

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Vilar¹,

Ferino²,

Quezada³

et al. 2013

CTMC

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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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QSAR of aromatic substances: MAO inhibitory activity of xanthone derivatives

Cited by 10 publications

References 13 publications

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

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

QSAR of heterocyclic antifungal agents by flip regression

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

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