BuildQSAR: A New Computer Program for QSAR Analysis

Oliveira, Daniel Barbosa de; Gaudio, Anderson Coser

doi:10.1002/1521-3838(200012)19:6<599::aid-qsar599>3.0.co;2-b

Cited by 156 publications

(86 citation statements)

References 5 publications

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“…By analyzing the parameters u and v with respect to the 23 descriptors with the GA-MLR technique in the BuildQSAR program [26] , respective optimal subset of descriptors in the model of parameters u and v was obtained. The optimal subset of descriptors for the parameter u comprises the average molecular polarizability (α), the HOMO energy of alpha spin states (E αHOMO ), and APT charge of C 1 atom (Q AC 1).…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

et al. 2013

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Abstract:In comparison with the Q-e scheme, the Revised Patterns Scheme: the U, V Version (the U-V scheme) has greatly improved both its accessibility and its accuracy in interpreting and predicting the reactivity of a monomer in free-radical copolymerizations. Quantitative structure-activity relationship (QSAR) models were developed to predict the reactivity parameters u and v of the U-V scheme, by applying genetic algorithm (GA) and support vector machine (SVM) techniques. Quantum chemical descriptors used for QSAR models were calculated from transition state species with structures C

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Section: Resultsmentioning

confidence: 99%

“…GA together with multiple linear regression (MLR) analysis has become an effective and powerful tool in selecting variables for QSARs. Thus GA-MLR technique in the BuildQSAR program [26] was used in this work. Next, the optimal descriptor sets were used as the input files of SVM models.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

et al. 2013

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“…The selected descriptors were used to build the 2D-QSPR models employing multiple linear regression (MLR) and leave-one-out (LOO) cross-validation method, performed by BuildQSAR® 1.0.0 software (Oliveira, Gaudio, 2003). Due to the relatively small size of the training set (n = 21), the statistical restriction which imposes a limit from four up to five observations (compounds) per descriptor or independent variable (Ferreira, 2002;Tavares, 2004) were respected in this approach and a maximum of four molecular descriptors per model was considered.…”

Section: Methodsmentioning

confidence: 99%

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Sá

Pasqualoto

Rangel-Yagui

2010

Braz. J. Pharm. Sci.

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Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/ brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.Uniterms: Two-dimensional quantitative structure-property relationships (2D-QSPR). Calculated n-octanol/water partition coefficient (ClogP). Blood-brain barrier. Benzodiazepines.Fármacos que atuam no sistema nervoso central (SNC) devem atravessar a barreira hematoencefálica (BHE) para exercerem suas ações farmacológicas. A difusão passiva através da BHE pode ser parcialmente expressa pelo coeficiente de partição entre os compartimentos encefálico e sanguíneo (logBB, brain/blood partition coefficient). Considerando-se que a avaliação experimental de logBB é dispendiosa e demorada, métodos teóricos como estudos das relações entre estrutura química e propriedade (QSPR, Quantitative Structure-Property Relationships) podem ser utilizados na previsão dos valores de logBB. Neste estudo, uma abordagem de QSPR-2D foi aplicada a um conjunto de 28 moléculas com ação central, usando logBB como propriedade biológica. O melhor modelo de QSPR [n = 21, r = 0,94 (r² = 0,88), s = 0,28 e Q² = 0,82] apresentou três descritores moleculares: o coeficiente calculado de partição n-octanol/água (ClogP), área de superfície polar (PSA) e polarizabilidade (α). Seis dos sete compostos do conjunto de avaliação foram bem previstos pelo modelo, o que corresponde a um bom poder de previsão externa (85,7%). Os resultados obtidos podem auxiliar de forma relevante em estudos futuros, orientando quais descritores moleculares devem ser considerados para estimar logBB e prever a passagem através da BHE de moléculas estruturalmente relacionadas às do conjunto investigado. Unitermos:Relações quantitativas bidimensionais entre estrutura química e propriedade (2D-QSPR). Coeficiente calculado de partição n-octanol/água (ClogP). Barreira hematoencefálica. Benzodiazepínicos.

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“…Preliminary models selection was performed by means of GA-MLRA technique as implemented in the BuildQSAR [20] program. As mentioned before, this approach allows selection of the models with the following characteristics: high quadratic correlation coefficient R 2 , low standard deviation S and the least number of descriptors involved.…”

Section: Methodsmentioning

confidence: 99%

A QSAR Toxicity Study of a Series of Alkaloids with the Lycoctonine Skeleton

Turabekova

Rasulev

2004

Molecules

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Abstract:A QSAR toxicity analysis has been performed for a series of 19 alkaloids with the lycoctonine skeleton. GA-MLRA (Genetic Algorithm combined with Multiple Linear Regression Analysis) technique was applied for the generation of two types of QSARs: first, models containing exclusively 3D-descriptors and second, models consisting of physicochemical descriptors. As expected, 3D-descriptor QSARs have better statistical fits. Physicochemical-descriptor containing models, that are in a good agreement with the mode of toxic action exerted by the alkaloids studied, have also been identified and discussed. In particular, TPSA (Topological Polar Surface Area) and nC=O (number of -C(O)-fragments) parameters give the best statistically significant mono-and bidescriptor models (when combined with lipophilicity, MlogP) confirming the importance of H-bonding capability of the alkaloids for binding at the receptor site.

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BuildQSAR: A New Computer Program for QSAR Analysis

Cited by 156 publications

References 5 publications

Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

A QSAR Toxicity Study of a Series of Alkaloids with the Lycoctonine Skeleton

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