Consensus model for identification of novel PI3K inhibitors in large chemical library

Liew, Chin Yee; Xiao, Hua; Yap, Chun Wei

doi:10.1007/s10822-010-9321-0

Cited by 14 publications

(9 citation statements)

References 36 publications

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“…In consequence, it has enjoyed substantial uptake (Nikora 2005;Camporelli 2006;Flikka et al 2006;Orhan and Altan 2006;Lasko et al 2006;Hunt 2006;Ferrari and Aitken 2006;Birzele and Kramer 2006;Kunchevaa et al 2007;Lau et al 2007;Masegosa et al 2007;Wang et al 2007;Garcia et al 2008;Tian et al 2008;Kurz et al 2009;Leon et al 2009;Shahri and Jamil 2009;Simpson et al 2009;Affendey et al 2010;García-Jiménez et al 2010;Hopfgartner et al 2010;Liew et al 2010). …”

Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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Averaged n-Dependence Estimators (AnDE) is an approach to probabilistic classification learning that learns by extrapolation from marginal to full-multivariate probability distributions. It utilizes a single parameter that transforms the approach between a lowvariance high-bias learner (Naive Bayes) and a high-variance low-bias learner with Bayes optimal asymptotic error. It extends the underlying strategy of Averaged One-Dependence Estimators (AODE), which relaxes the Naive Bayes independence assumption while retaining many of Naive Bayes' desirable computational and theoretical properties. AnDE further relaxes the independence assumption by generalizing AODE to higher-levels of dependence. Extensive experimental evaluation shows that the bias-variance trade-off for Averaged 2-Dependence Estimators results in strong predictive accuracy over a wide range of data sets. It has training time linear with respect to the number of examples, learns in a single pass through the training data, supports incremental learning, handles directly missing values, and is robust in the face of noise. Beyond the practical utility of its lower-dimensional variants, AnDE is of interest in that it demonstrates that it is possible to create low-bias high-variance generative learners and suggests strategies for developing even more powerful classifiers.

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Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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“…These individual (constituent) classifiers, which were referred to as base classifiers or base models in the manuscript, form the ''bottom layer'' where the ensemble method is applied on. The ensemble method has shown better performances of varying degree in quantitative structure-activity relationship (QSAR) studies [30][31][32][33][34] and quantitative structuretoxicity relationship (QSTR) studies [35][36][37][38] compared to predictions from a single classifier. There is an assortment of approaches to generate multiple models that form the base classifiers of an ensemble model [39].…”

Section: Introductionmentioning

confidence: 99%

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

Liew

Lim

Yap

2011

J Comput Aided Mol Des

Self Cite

101

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Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

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“…[6] Although AD is of great importance, compared with other aspects of QSAR, e.g. variable selection, [7][8][9] modeling techniques, [10][11][12] model validation, [13][14][15] model application, [16][17][18] the study on AD is still not enough. In the past years, some interesting studies have been reported, [19][20][21][22][23][24][25][26][27][28][29][30] but the problem of AD definition has not been well resolved so far.…”

Section: Introductionmentioning

confidence: 99%

A Combinational Strategy of Model Disturbance and Outlier Comparison to Define Applicability Domain in Quantitative Structural Activity Relationship

Yan

Zhu

Kong

et al. 2014

Molecular Informatics

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In order to define an applicability domain for quantitative structure-activity relationship modeling, a combinational strategy of model disturbance and outlier comparison is developed. An indicator named model disturbance index was defined to estimate the prediction error. Moreover, the information of the outliers in the training set was used to filter the unreliable samples in the test set based on "structural similarity". Chromatography retention indices data were used to investigate this approach. The relationship between model disturbance index and prediction error can be found. Also, the comparison between the outlier set and the test set could provide additional information about which unknown samples should be paid more attentions. A novel technique based on model population analysis was used to evaluate the validity of applicability domain. Finally, three commonly used methods, i.e. Leverage, descriptor range-based and model perturbation method, were compared with the proposed approach.

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Consensus model for identification of novel PI3K inhibitors in large chemical library

Cited by 14 publications

References 36 publications

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

A Combinational Strategy of Model Disturbance and Outlier Comparison to Define Applicability Domain in Quantitative Structural Activity Relationship

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