LQTA-QSAR: A New 4D-QSAR Methodology

Martins, João Paulo Ataide; Barbosa, Euzébio Guimarães; Pasqualoto, Kerly Fernanda Mesquita; Ferreira, Márcia M. C.

doi:10.1021/ci900014f

Cited by 79 publications

(64 citation statements)

References 30 publications

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“…Partial least squares (PLS; Dijkstra, 2010) are one of the chemometric methods commonly envisaged in many QSAR models. In present work, 4D-QSAR approach called LQTAgrid-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada) introduced by Martins et al (2009) has been used to build a 4D-QSAR model. Such 4D-QSAR model was developed in a search of important structural features required in the coumarin derivatives and subsequent design of more active molecules.…”

Section: Introductionmentioning

confidence: 99%

4D-QSAR studies of coumarin derivatives as HIV-1 integrase 3′-processing inhibitors

Patil

Sawant

2015

Med Chem Res

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In a pursuit of combating acquired immunodeficiency syndrome, the targets human immunodeficiency virus (HIV) protease, reverse transcriptase and HIV specific proteins were exhaustively explored. Resistance through mutations in these targets has impeded the further developments in HIV research. HIV-1 integrase (HIV-1 IN) has emerged as a newer target and 3 0 -processing, and strand transfer inhibition has proved potential intervention in HIV replication. In present study, in line with our interests in coumarin derivatives as potential bioactive molecules, 4D-quantitative structure-activity relationship (4D-QSAR) is proposed. Fifty-seven structurally diverse coumarin derivatives were subjected to 4D-QSAR studies. Quantum mechanics-based geometry optimization and molecular dynamics simulation were carried out on individual compound. The conformational ensemble generated for each compound was aligned with most active compound, and Coulombic and Lennard-Jones interaction energy descriptors were computed. After selecting the best variables in MATLAB, partial least square regression (PLS) analysis was carried out on 44 training set and 13 test set compounds. The model with ten latent variables was found best with R 2 calculated = 0.903015, R 2 crossvalidated = 0.599553, R 2 predicted = 0.688525, rootmean-square error (RMSE) calculated = 0.21276, RMSE predicted = 0.371579 and prediction bias = -0.15362. Docking studies were carried out on AutoDock Vina, which were in good agreement with the PLS model, suggesting the importance of few descriptors of Coulombic interaction energy and VWD interactions with VAL79, VAL77, ARG199 and GLU157. These results may be useful in designing more potent HIV-1 IN inhibitors.

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Section: Introductionmentioning

confidence: 99%

4D-QSAR studies of coumarin derivatives as HIV-1 integrase 3′-processing inhibitors

Patil

Sawant

2015

Med Chem Res

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“…Analysis using the OPS (ordered predictor selection) algorithm was applied as the variable selection method in the construction of the PLS models (Martins et al, 2009). First, the method obtains an informative vector with length equal to the number of response variables and each position in the vector is aligned to the best corresponding response variables to predict the location of the best response.…”

Section: D-qsar Model Calculations: Methodsmentioning

confidence: 99%

“…The GCODs from alignment 3 were available though the OPS-PLS algorithm developed by Martins et al (2009). The best model (Model 2) has eight descriptors and the statistical significance of the relationship between the biological response and the chemical structure descriptors was further demonstrated by a cross-validation analysis.…”

Section: Qsar: Methodsmentioning

confidence: 99%

Molecular modeling of the Toxoplasma gondii adenosine kinase inhibitors

2011

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Toxoplasma gondii is the most common cause of secondary central nervous system infection in immunocompromised people such as AIDS patients. Since purine salvage is essential for T. gondii and other parasitic protozoa, inhibition of its salvage pathway, by blocking adenosine kinase (EC 2.7.1.20), the main responsible for the metabolism of adenosine (purine nucleoside) can block the parasite growth. Having this in mind, four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied to a series of 41 inhibitors of T. gondii adenosine kinase. Optimized 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by the leave-one-out cross-validation method. Moreover, we have used docking approaches to study the binding orientations and predict the interaction energies of some benzyladenosines with adenosine kinase.

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“…Entretanto, programas modernos de modelagem usados em estudos de QSAR geram milhares de descritores que frequentemente são altamente correlacionados entre si, especialmente em análises de QSAR 3D e 4D. [24][25][26][27] Assim, o método MLR não pode ser usado nesses casos, a menos que se faça uma seleção de variáveis criteriosa. Para evitar esses problemas, uma boa alternativa é o uso dos métodos de projeção, também conhecidos como métodos bilineares, como a regressão por componentes principais (PCR) ou a regressão por quadrados mínimos parciais (PLS).…”

Section: Construção De Modelos De Regressão Com O Método Plsunclassified

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

Martins¹,

Ferreira²

2013

Quím. Nova

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Recebido em 4/6/12; aceito em 15/11/12; publicado na web em 12/3/13 QSAR MODELING: A NEW OPEN SOURCE COMPUTATIONAL PACKAGE TO GENERATE AND VALIDATE QSAR MODELS. QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure-activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta. iqm.unicamp.br.Keywords: QSAR models; OPS variable selection; outlier detection. INTRODUÇÃOO estudo das relações quantitativas entre a estrutura química e a atividade biológica ou alguma propriedade físico-química (QSAR/ QSPR) é uma área de destaque hoje na comunidade científica. Por exemplo, na área da físico-química estudos de QSPR são essenciais na predição de propriedades que são difíceis de serem medidas experimentalmente. Já na área de química medicinal teórica, a predição da atividade biológica de novos compostos usando relações matemáticas baseadas em propriedades estruturais, físico-químicas e conformacionais de potenciais agentes previamente testados é um campo de pesquisa extremamente ativo e promissor. Relações QSAR são úteis para entender e explicar o mecanismo de ação de fármacos em nível molecular e permite o projeto e o desenvolvimento de novos compostos com propriedades biológicas desejáveis. 1 Um modelo quantitativo QSAR (ou QSPR) é representado por meio de uma equação matemática que relaciona as propriedades dos compostos investigados com suas atividades biológicas e que possui significância estatística. Essa equação deve não somente possuir um bom poder de predição, mas deve também ser validada mostrando-se robusta e não obtida ao acaso. 2-7Existem diversos programas disponíveis na literatura que podem ser utilizados para gerar modelos QSAR. 17 A Tabela 1 mostra uma comparação das principais características presentes no programa QSAR modeling com os programas supracitados. É notório que dentre os programas livres, apenas o QSAR modeling incorpora todos os testes sugeridos na literatura para a validação 3 e obtenção de modelos robustos, não obtidos por correlações espúrias e com a avaliação crítica dos compostos com comportamento atípico.Neste trabalho, é apresentado um novo programa open source, denominado QSAR modeling, cujo objetivo é construir e validar modelos de QSAR utilizando as ferramentas quimiométricas. Esse é o primeiro programa que implementa o método de seleção de variáveis recentemente desenvolvido ordered predictors selection (OPS), 18 incorpora os processos de vali...

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LQTA-QSAR: A New 4D-QSAR Methodology

Cited by 79 publications

References 30 publications

4D-QSAR studies of coumarin derivatives as HIV-1 integrase 3′-processing inhibitors

4D-QSAR studies of coumarin derivatives as HIV-1 integrase 3′-processing inhibitors

Molecular modeling of the Toxoplasma gondii adenosine kinase inhibitors

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

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