Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

Cui, Wentong; Yan, Xuefeng

doi:10.1016/j.chemolab.2009.05.008

Cited by 57 publications

(21 citation statements)

References 7 publications

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“…6. With a correlation coefficient of 0.82 and a cross-validated correlation coefficient of 0.74, the established QSAR model was acceptable (64). This model was used to predict the Ki values for 14 compounds among the top 20 in silico synthesized compounds, as listed in Table II.…”

Section: Resultsmentioning

confidence: 97%

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

Bian

Feng

Yang

et al. 2017

AAPS J

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GPCR allosteric modulators target at the allosteric binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an orthosteric ligand. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of oversensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu5), a member of class C GPCRs, is a promising therapeutic target for treating many central nervous system diseases. The crystal structure of mGlu5 in the complex with the negative allosteric modulator mavoglurant was recently reported, providing a fundamental model for designing new allosteric modulators. Computational fragment-based drug discovery represents a powerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the compounds generated were assessed by benchmark dataset verification, docking studies, and QSAR model simulation. Subsequently, structurally diverse compounds, with reported or unreported scaffolds, can be observed from top 20 in silico synthesized compounds, which were predicted to be potential mGlu5 modulators. In silico compounds with reported scaffolds may fill SAR holes in known, patented series of mGlu5 modulators. And the generation of compounds without reported tests on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu5 demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design in the future.

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Section: Resultsmentioning

confidence: 97%

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

Bian

Feng

Yang

et al. 2017

AAPS J

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“…In the second stage, the remaining data were used to train SVRs. For example, Cui and Yan (2009) developed an adaptive weighted LS-SVR, which first used the robust 3σ principle to remove outliers in the training data set and then a weighted LS-SVR was applied to the remaining samples. Wen et al (2010) presented a recursive training-eliminating procedure for robust regression, where sample points simulated by LS-SVR with large errors were recursively removed until the sum of a certain number of sorted absolute errors did not increase.…”

Section: Introductionmentioning

confidence: 99%

A robust algorithm of support vector regression with a trimmed Huber loss function in the primal

et al. 2016

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Support vector machine for regression (SVR) is an efficient tool for solving function estimation problem. However, it is sensitive to outliers due to its unbounded loss function. In order to reduce the effect of outliers, we propose a robust SVR with a trimmed Huber loss function (SVRT) in this paper. Synthetic and benchmark datasets were, respectively, employed to comparatively assess the performance of SVRT, and its results were compared with those of SVR, least squares SVR (LS-SVR) and a weighted LS-SVR. The numerical test shows that when training samples are subject to errors with a normal distribution, SVRT is slightly less accurate than SVR and LS-SVR, yet more accurate than the weighted LS-SVR. However, when training samples are contaminated by outliers, SVRT has a better performance than the other methods. Furthermore, SVRT is faster than the weighted LS-SVR. Simulating eight benchmark datasets shows that SVRT is averagely more accurate than the other methods when sample points are contaminated by outliers. In Communicated by A. conclusion, SVRT can be considered as an alternative robust method for simulating contaminated sample points.

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“…Support vector machines (SVM) introduced by Vapnik are a new methodology in the area of nonlinear modeling after neural networks. While neural networks suffer from problems like the existence of many local minima and the choice of the number of hidden units [6], SVM are characterized by convex optimization problems based on sound theoretical principles, up to the determination of a few additional tuning parameters, and provide better generalization performance than that of neural networks [7,8]. The convex quadratic programming problem is solved in dual space in order to determine the SVM model.…”

Section: Introductionmentioning

confidence: 99%

Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems

Sun

2010

Neural Comput & Applic

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To improve the robustness of the traditional inverse system method, the internal model control based on a novel least square support vector machines (LS-SVM) is proposed. The novel LS-SVM considers general errors that include noises of input variables and output variables as empirical errors. The data of original MIMO discrete system is exploited to approximate its inverse model by the novel LS-SVM. By cascading the inverse model and the original system to constitute a decoupling pseudo-linear system, the internal model control strategy is carried out to the pseudo-linear system to realize the effective control. Simulation validates that the novel LS-SVM used in the inverse system identification is effective and shows that the internal model control of nonlinear discrete systems has better robustness of anti-interference and parameters varying than that of the open-loop system only based on inverse control.

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Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

Cited by 57 publications

References 7 publications

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

A robust algorithm of support vector regression with a trimmed Huber loss function in the primal

Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems

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