An accurate nonlinear QSAR model for the antitumor activities of chloroethylnitrosoureas using neural networks

Qin, Yu; Deng, Hongfei; Yan, Hong; Zhong, Rugang

doi:10.1016/j.jmgm.2011.01.007

Cited by 11 publications

(10 citation statements)

References 25 publications

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“…We would be remiss not to mention that there are several other approaches to dealing with the large-p, small-n problem including PLS [36,37] and Bayesian neural networks [64,65,66]. Bayesian neural networks can provide extremely good predictive power under cross-validation scrutiny, but we prefer the interpretability afforded regression models which can lead to mechanistic understanding of how structure affects activity.…”

Section: Discussionmentioning

confidence: 99%

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

2015

Advances in Mathematical Chemistry and Applications

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A primary goal of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) is to predict chemical activities from chemical structure. Chemical structure can be quantified in many ways resulting in hundreds, if not thousands, of measurements for every chemical. Chemical activities measures how the chemical interacts with other chemicals, e.g. toxicity, biodegradability, boiling point, and vapor pressure. Typically there are more chemical structure measurements than chemicals being measured, the so-called large-p, smalln problem. Here we review some of the statistical procedures that have been commonly used to explore these problems in the past and provide several examples of their use. Finally, we peek into the future to discuss two areas that we believe will see dramatically increased attention in the near future: model averaging and Bayesian techniques.

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Section: Discussionmentioning

confidence: 99%

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

2015

Advances in Mathematical Chemistry and Applications

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“…The best obtained ANN model contains 2 hidden neurons and was the best model to predict the test set (r 2 = 0.907 and r 2 test = 0.770); the SVM technique was the most accurate model to the training set (r 2 = 0.913 and r 2 test = 0.731). Qin et al [97] constructed and compared ANN and MLR models of chloroethylnitrosoureas (CENUs), which are potent alkylanting agents and employed against some kinds of tumors (as leukemias, melanomas, encephalomas and some solid tumors), since they can modify some nucleosides (possible source of citotoxicity). All selected descriptors described the interactions related to the DNA alkylation.…”

Section: Comparison Of Methodsmentioning

confidence: 99%

“…However, several nonlinear QSAR methods have been proposed in recent years [32][33][34]. In QSAR methods based on regression analysis, it is necessary to previously assume an inputoutput relation (e.g.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Machine Learning Techniques and Drug Design

Gertrudes¹,

Maltarollo²,

Silva

et al. 2012

CMC

167

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The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

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“…. 氯乙基亚硝基脲的构效关系研究表明, 当亚硝基脲和刚性的多面体烷结合后, 可以有效地提高亚硝基脲类化合物的抗癌活性 [6] . 此外, 亚硝基脲作为治疗脑部肿瘤的有效药物, 多面体烷的高脂溶性使其穿越血脑屏障的能力大大增加, 从而可以进一步提高亚硝基脲类烷化剂对脑部肿瘤的疗效 [7] .…”

Section: 癌的机理研究表明适当的 β-位活性以及两个活性区unclassified

Synthesis of Polyhedronylcarbonyl Chloroethylureas Using Acylfluoride in Ionic Liquid

Zhang¹,

Zhu²,

Yan³

et al. 2012

Chin. J. Org. Chem.

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An accurate nonlinear QSAR model for the antitumor activities of chloroethylnitrosoureas using neural networks

Cited by 11 publications

References 25 publications

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

Machine Learning Techniques and Drug Design

Synthesis of Polyhedronylcarbonyl Chloroethylureas Using Acylfluoride in Ionic Liquid

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