Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method

Ma, C; Yang, Shuyuan; Zhang, Hui; Xiang, Mingli; Huang, Qi; Wei, Yuquan

doi:10.1016/j.jpba.2008.03.023

Cited by 55 publications

(35 citation statements)

References 28 publications

(41 reference statements)

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“…There is still no consensus on which cutoffs should be used to form the classes. The following cutoffs have ever been used to construct two-class classifiers: 20% by Ma et al (if HOBA>=20%, then the molecule belongs to the ‘+’ class, otherwise, it is in ‘−‘ class), 31 30% by ourselves, 36 and 50% by Olivares-Morales et al 39 Few classifiers were published for more than two classes. 26 To construct a successful classifier, the number of data in each classes should be balanced, otherwise, one may have good sensitivity but poor specificity or vice versa.…”

Section: Discussionmentioning

confidence: 99%

“…31 GA was applied to select descriptors that were calculated using Cerius 2 software package (http://www.accelyrs.com), while SVM was used to construct classification model and CG was applied to optimize the parameters of kernel functions of SVM. The predict accuracy, 80% for the training set (690 compounds) and 86% for the test set (76 compounds) is encouraging.…”

Section: Recent Advances In Hoba Modelingmentioning

confidence: 99%

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Advances in computationally modeling human oral bioavailability

Wang

Hou

2015

Advanced Drug Delivery Reviews

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Although significant progress has been made in experimental high throughput screening (HTS) of ADME (absorption, distribution, metabolism, excretion) and pharmacokinetic properties, the ADME and Toxicity (ADME-Tox) in silico modeling is still indispensable in drug discovery as it can guide us to wisely select drug candidates prior to expensive ADME screenings and clinical trials. Compared to other ADME-Tox properties, human oral bioavailability (HOBA) is particularly important but extremely difficult to predict. In this paper, the advances in human oral bioavailability modeling will be reviewed. Moreover, our deep insight on how to construct more accurate and reliable HOBA QSAR and classification models will also discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Recent Advances In Hoba Modelingmentioning

confidence: 99%

Advances in computationally modeling human oral bioavailability

Wang

Hou

2015

Advanced Drug Delivery Reviews

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“…Recently, another supervised learning machine method, the support vector machine (SVM), has been widely employed as a classification technique in the prediction of absorption properties (Liu et al, 2005;Guangli and Yiyu, 2006;Hou, 2007;Ma et al, 2008;Yan et al, 2008). The SVM attempts to find a boundary or hyperplane that separates two classes of compounds.…”

Section: Statistical Toolsmentioning

confidence: 99%

Computational and Pharmacoinformatic Approaches to Oral Bioavailability Prediction

Cabrera‐Pérez

González‐Álvarez

2011

Oral Bioavailability

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“…GA has been successfully applied to a series of problems such as data mining and optimization. It has also been used for the feature selection in SVM modeling [4] . As to a specific problem, The GA looks a solution as an individual chromosome.…”

Section: A Support Vector Regression(svr)mentioning

confidence: 99%

Application of an integrated support vector regression method in prediction of financial returns

Cheng²

2011

IJIEEB

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Abstract--Nowadays there are lots of novel forecasting approaches to improve the forecasting accuracy in the financial markets. Support Vector Machine (SVM) as a modern statistical tool has been successfully used to solve nonlinear regression and time series problem. Unlike most conventional neural network models which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error rather than minimizing the training error. To build an effective SVM model, SVM parameters must be set carefully. This study proposes a novel approach, support vector machine method combined with genetic algorithm (GA) for feature selection and chaotic particle swarm optimization(CPSO) for parameter optimization support vector Regression(SVR),to predict financial returns. The advantage of the GA-CPSO-SVR (Support Vector Regression) is that it can deal with feature selection and SVM parameter optimization simultaneously A numerical example is employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in forecasting financial returns.

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Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method

Cited by 55 publications

References 28 publications

Advances in computationally modeling human oral bioavailability

Advances in computationally modeling human oral bioavailability

Computational and Pharmacoinformatic Approaches to Oral Bioavailability Prediction

Application of an integrated support vector regression method in prediction of financial returns

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