Active Learning with Support Vector Machines in the Drug Discovery Process.

Warmuth, Manfred K.; Liao, Jun; Raetsch, Gunnar; Mathieson, Michael; Putta, Santosh; Lemmen, Christian

doi:10.1002/chin.200322232

Cited by 152 publications

(207 citation statements)

References 3 publications

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“…13 SVMs were originally developed for binary classification problems and have become popular in the chemoinformatics field. [16][17][18] In a typical SVM analysis, training compounds belonging to two different classes (e.g., active versus inactive) are projected into chemical reference space and a separating hyperplane is derived. Then, test compounds are evaluated in this reference space to predict their class labels dependent on which side of the hyperplane they fall.…”

Section: Introductionmentioning

confidence: 99%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

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Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.

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Section: Introductionmentioning

confidence: 99%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…SVMs have also been applied in chemistry, for example, the prediction of retention index of protein, and other QSAR studies. [13][14][15][16][17][18][19][20][21] Compared with traditional regression and neural networks methods, SVMs have some advantages, including global optimum, good generalization ability, simple implementation, few free parameters, and dimensional independence. [22][23][24] The flexibility in classification and ability to approximate continuous function make SVMs very suitable for QSAR and QSPR studies.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Yao

Panaye

Doucet

et al. 2004

J. Chem. Inf. Comput. Sci.

223

115

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Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

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“…Due to its remarkable generalization performance, the SVM has attracted attention and gained extensive application. [9][10][11][12][13][14][15] Based on the Structural Risk Minimization principle which seeks to minimize an upper bound of the generalization error rather than minimize the empirical error commonly implemented in other neural networks, SVMs achieve a higher generalization performance than traditional neural networks in solving these machine learning problems. Another key property is that unlike the training of other networks, which requires nonlinear optimization with the danger of getting stuck in local minima, training SVMs is equivalent to solving a linearly constrained quadratic programming problem.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

Liu

Zhang

Yao

et al. 2003

J. Chem. Inf. Comput. Sci.

126

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The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R ) 0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

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Active Learning with Support Vector Machines in the Drug Discovery Process.

Cited by 152 publications

References 3 publications

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

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