CP-MLR directed QSAR studies on the antimycobacterial activity of functionalized alkenols—topological descriptors in modeling the activity

Gupta, Maneesh K.; Sagar, Ram; Shaw, Arun K.; Prabhakar, Yenamandra S.

doi:10.1016/j.bmc.2004.10.025

Cited by 44 publications

(31 citation statements)

References 19 publications

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“…The CP-MLR is a 'filter' based variable selection procedure for model development in QSAR studies [30][31][32][33][34]. The procedure employs a combinatorial strategy with MLR to result in selected subset regressions for the extraction of diverse structure-activity models, each having unique combination of descriptors from the generated data set of the compounds under study.…”

Section: Model Developmentmentioning

confidence: 99%

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

Sharma¹,

Pilania²,

Singh³

et al. 2009

Open Chemistry

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Abstract:The inhibition activities of sulfonamide and sulfamate derivatives for human carbonic anhydrases have been quantitatively analyzed using DRAGON descriptors. QSAR models have been obtained through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. For the hCA I inhibition activity, a higher value of information content index of the 1-order neighborhood symmetry (IC1) and a lower value of the Moran autocorrelations, MATS2v and MATS1p, along with a lower number of sulfur atoms in a molecular structure (nRSR) is beneficial to the activity. A higher number of 5-membered rings (nR05), a bigger distance between nitrogen and sulfur T(N..S), and a higher value of van der Waals volume weighted descriptor (GATS6v), are helpful to improve the hCA II inhibition activity. For the inhibition of hpCA, a lower value of the descriptors Jhetv and PW5, and higher values of the eigenvalue sum from Z weighted distance matrix, SEigZ, the Moran autocorrelation of lag 8 weighted by atomic van der Waals volumes, MATS8v and the Moran autocorrelation of lag 4 weighted by atomic Sanderson electronegativities, MATS4e are favorable. The derived significant models in such descriptors may further be used to synthesize new potential compounds and to decipher the mode of their actions at molecular level.

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Section: Model Developmentmentioning

confidence: 99%

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

Sharma¹,

Pilania²,

Singh³

et al. 2009

Open Chemistry

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show abstract

“…The information content of these and many other graph theoretical descriptors have been validated through correlation studies involving different additive-constitutive properties of hydrocarbons [70][71][72] and biological properties of several classes of compounds [73][74][75][76]. An interesting application of graph theory is the explanation and prediction of 13 C-NMR profile of organic compounds [77].…”

Section: Graph Theoretical Indicesmentioning

confidence: 99%

Chemical Structure Indices in In Silico Molecular Design

Prabhakar

2008

Sci Pharm

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“…The quantitative structure-activity relationship (QSAR) studies offer the structural requirements of the probe molecules for an activity. They are successfully used in the development of rationales for divergent biological activities [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Juglone derivatives as antitubercular agents: A rationale for the activity profile

Sharma

Prabhakar

2009

European Journal of Medicinal Chemistry

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CP-MLR directed QSAR studies on the antimycobacterial activity of functionalized alkenols—topological descriptors in modeling the activity

Cited by 44 publications

References 19 publications

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

Chemical Structure Indices in In Silico Molecular Design

Juglone derivatives as antitubercular agents: A rationale for the activity profile

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