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A ranking method for the concurrent learning of compounds with various activity profiles.

Dörr A, Rosenbaum L, Zell A - J Cheminform (2015)

Bottom Line: The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening.Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics Tübingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076 Germany.

ABSTRACT

Background: In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each.

Results: The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.

Conclusions: SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

No MeSH data available.


Related in: MedlinePlus

Support vector classification (SVC). Illustration of an SVC classification function represented by wTx. The slack variables ξi=yiwTx facilitate the trade-off between the size of the margin (indicated by a gray tube) and the error due to misclassifications. w denotes the weight vector, yi the label of instance i, and x is the feature vector. ξi can assume a positive value between 0 and for 1 for training instances located in the margin. For instances on the wrong side of the margin ξi is less than 0. Support vectors are indicated by a red ring.
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Fig2: Support vector classification (SVC). Illustration of an SVC classification function represented by wTx. The slack variables ξi=yiwTx facilitate the trade-off between the size of the margin (indicated by a gray tube) and the error due to misclassifications. w denotes the weight vector, yi the label of instance i, and x is the feature vector. ξi can assume a positive value between 0 and for 1 for training instances located in the margin. For instances on the wrong side of the margin ξi is less than 0. Support vectors are indicated by a red ring.

Mentions: The concept of the linear combination of SVM models was introduced by Geppert et al. [28] and applied in a recent study of Heikamp et al. [27] to the prediction of active molecules with respect to overlapping activity profiles against multiple targets. This approach is based on a combination of the weight vectors w of separate SVM models to a single united weight vector wcombined. Equation 1 shows the linear combination of n weight vectors, where wi is the weight vector of the i-th model and ci its linear factor. A single SVM model is described in Figure 2. (1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ \mathbf{w}_{combined} = \sum\limits_{i=1}^{n} c_{i} \mathbf{w}_{i} $$ \end{document}wcombined=∑i=1nciwiFigure 2


A ranking method for the concurrent learning of compounds with various activity profiles.

Dörr A, Rosenbaum L, Zell A - J Cheminform (2015)

Support vector classification (SVC). Illustration of an SVC classification function represented by wTx. The slack variables ξi=yiwTx facilitate the trade-off between the size of the margin (indicated by a gray tube) and the error due to misclassifications. w denotes the weight vector, yi the label of instance i, and x is the feature vector. ξi can assume a positive value between 0 and for 1 for training instances located in the margin. For instances on the wrong side of the margin ξi is less than 0. Support vectors are indicated by a red ring.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4306736&req=5

Fig2: Support vector classification (SVC). Illustration of an SVC classification function represented by wTx. The slack variables ξi=yiwTx facilitate the trade-off between the size of the margin (indicated by a gray tube) and the error due to misclassifications. w denotes the weight vector, yi the label of instance i, and x is the feature vector. ξi can assume a positive value between 0 and for 1 for training instances located in the margin. For instances on the wrong side of the margin ξi is less than 0. Support vectors are indicated by a red ring.
Mentions: The concept of the linear combination of SVM models was introduced by Geppert et al. [28] and applied in a recent study of Heikamp et al. [27] to the prediction of active molecules with respect to overlapping activity profiles against multiple targets. This approach is based on a combination of the weight vectors w of separate SVM models to a single united weight vector wcombined. Equation 1 shows the linear combination of n weight vectors, where wi is the weight vector of the i-th model and ci its linear factor. A single SVM model is described in Figure 2. (1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ \mathbf{w}_{combined} = \sum\limits_{i=1}^{n} c_{i} \mathbf{w}_{i} $$ \end{document}wcombined=∑i=1nciwiFigure 2

Bottom Line: The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening.Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics Tübingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076 Germany.

ABSTRACT

Background: In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each.

Results: The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.

Conclusions: SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

No MeSH data available.


Related in: MedlinePlus