Interpretation of Quantitative Structure−Property and −Activity Relationships

Katritzky, Alan R.; Petrukhin, Ruslan; Tatham, Douglas B.; Basak, Subhash C.; Benfenati, Emilio; Karelson, Mati; Maran, Uko

doi:10.1021/ci000134w

Cited by 113 publications

(70 citation statements)

References 54 publications

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“…3 Recently we have commenced a general analysis of these issues using a combination of chemometric techniques such as multilinear regression analysis (MLR) and principal component analysis (PCA). 4 Our earlier efforts to use PCA to explain physicochemical phenomena have been successful. In an application of the PCA method to a chemical problem, we treated aromaticity as a multidimensional entity and found that the magnetic and structural components are orthogonal.…”

Section: Introductionmentioning

confidence: 99%

A General Treatment of Solubility. 3. Principal Component Analysis (PCA) of the Solubilities of Diverse Solutes in Diverse Solvents

Katritzky

Tulp

Fara

et al. 2005

J. Chem. Inf. Model.

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A phenomenological study of solubility has been conducted using a combination of quantitative structureproperty relationship (QSPR) and principal component analysis (PCA). A solubility database of 4540 experimental data points was used that utilized available experimental data into a matrix of 154 solvents times 397 solutes. Methodology in which QSPR and PCA are combined was developed to predict the missing values and to fill the data matrix. PCA on the resulting filled matrix, where solutes are observations and solvents are variables, shows 92.55% of coverage with three principal components. The corresponding transposed matrix, in which solvents are observations and solutes are variables, showed 62.96% of coverage with four principal components.

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Section: Introductionmentioning

confidence: 99%

A General Treatment of Solubility. 3. Principal Component Analysis (PCA) of the Solubilities of Diverse Solutes in Diverse Solvents

Katritzky

Tulp

Fara

et al. 2005

J. Chem. Inf. Model.

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“…Mesmo que a não interpretação de um modelo não impeça sua utilização para fins de predição, caso seja possível interpretá-lo, reforça a utilidade da equação obtida para explicar o problema em estudo. 23,[41][42][43][44] Outro fato importante a ser considerado neste estudo é que o mecanismo de ação dos compostos estudados contra o P. falciparum 3D7 não é conhecido. Assim, a interpretação mecanística pode ser baseada apenas nos descritores moleculares selecionados, seus significados gerais e por comparação com os resultados de outros estudos QSAR.…”

Section: Figura 4 Resultados Dos Estudos De Validação Cruzada Lno (Aunclassified

COMPUTACIONAL STUDY OF 1H-IMIDAZOL-2-YL-PYRIMIDINE-4,6-DIAMINES FOR IDENTIFICATION OF POTENTIAL PARENT COMPOUNDS OF NEW ANTIMALARIAL AGENTS

2016

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Recebido em 24/10/2015; aceito em 01/02/2016; publicado na web em 15/04/2016 COMPUTACIONAL STUDY OF 1H-IMIDAZOL-2-YL-PYRIMIDINE-4,6-DIAMINES FOR IDENTIFICATION OF POTENTIAL PARENT COMPOUNDS OF NEW ANTIMALARIAL AGENTS. The aim of this study was develop in silico studies with a data set of 1H-imidazol-2-yl-pyrimidine-4,6-diamines derivatives described as antimalarial agents with the intention of obtain data and tools useful in the development of new drugs of this class. A QSAR model was developed using topological, geometrical and electronic molecular descriptors with aid of the OPS/PLS approach. The data set was also used for pharmacophoric modeling, that was used in the ZINC database for found compounds that show good fit with the model. Due to the similarity between the four selected hits in the virtual screening with the data set, the obtained equation was used for predict the potential antimalarial activity. All hits were within the applicablity domain of the QSAR model, and showed adquates Monge's leadlikeness score and predicted safety profiles.

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“…(Katritzky et al, 2001), as it does not impose any restriction on the type and number of graphical invariants used in structureproperty-activity studies. For a valid statistical significance of the MRA, it is necessary to restrict the maximal number of descriptors, which depends on the number of compounds investigated (Rao, 1973;Randic, 2001) To avoid ambiguities in the interpretation of regression, only a few parameters, or ideally a single parameter, may be used.…”

Section: Discussionmentioning

confidence: 99%

QSAR Analysis of BABQ compounds via calculated molecular descriptors

Nandi

Bagchi

2007

Med Chem Res

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We attempted to formulate quantitative structure-activity relationship modeling of 2,5-bis(1-Aziridinyl) 1,4-benzoquinone (BABQ) compounds according to calculated molecular descriptors. Various molecular descriptors such as physicochemical, constitutional, geometrical, electrostatic, and topological indices of such compounds have been calculated and QSAR models have been developed considering in vitro and in vivo biological activities. To establish a relationship between activity and structural descriptors of BABQ compounds, it is essential to develop a regression or an inputoutput model. Because the number of molecular descriptors greatly exceeds the number of observations, conventional regression methodologies are not useful in such studies. Hence, we developed QSAR models based on a large set of theoretical molecular descriptors using ridge regression methodology, which overcomes this limitation and also because the independent variables are highly intercorrelated. Finally, we applied the model for prediction of a promising new BABQ compound expected to be highly active, and it is seen that our model is in good agreement with the hypothesis in terms of in vitro and in vivo activities.

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Interpretation of Quantitative Structure−Property and −Activity Relationships

Cited by 113 publications

References 54 publications

A General Treatment of Solubility. 3. Principal Component Analysis (PCA) of the Solubilities of Diverse Solutes in Diverse Solvents

A General Treatment of Solubility. 3. Principal Component Analysis (PCA) of the Solubilities of Diverse Solutes in Diverse Solvents

COMPUTACIONAL STUDY OF 1H-IMIDAZOL-2-YL-PYRIMIDINE-4,6-DIAMINES FOR IDENTIFICATION OF POTENTIAL PARENT COMPOUNDS OF NEW ANTIMALARIAL AGENTS

QSAR Analysis of BABQ compounds via calculated molecular descriptors

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