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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

Performance of the cytochrome P450 and dehydrogenase baseline data sets for dual-target activity. Each boxplot depicts the mean ranking error on the 20 randomly generated test sets for each target. The given ranking error is equal to 1−AUC.
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Fig7: Performance of the cytochrome P450 and dehydrogenase baseline data sets for dual-target activity. Each boxplot depicts the mean ranking error on the 20 randomly generated test sets for each target. The given ranking error is equal to 1−AUC.

Mentions: The results of the baseline data sets are depicted in Figures 6 and 7. As with all other data sets used in this study 20 training and test sets were sampled at random and the mean ranking error was calculated. The performances of the different baseline data sets are consistent with the results of Agarwal et al. [23] for a binary labeling of training and test sets. The linear ranking SVM can compete with the standard linear SVM and shows a slightly better performance for some targets, especially on the data sets for dual-target activity.Figure 6


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

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

Performance of the cytochrome P450 and dehydrogenase baseline data sets for dual-target activity. Each boxplot depicts the mean ranking error on the 20 randomly generated test sets for each target. The given ranking error is equal to 1−AUC.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig7: Performance of the cytochrome P450 and dehydrogenase baseline data sets for dual-target activity. Each boxplot depicts the mean ranking error on the 20 randomly generated test sets for each target. The given ranking error is equal to 1−AUC.
Mentions: The results of the baseline data sets are depicted in Figures 6 and 7. As with all other data sets used in this study 20 training and test sets were sampled at random and the mean ranking error was calculated. The performances of the different baseline data sets are consistent with the results of Agarwal et al. [23] for a binary labeling of training and test sets. The linear ranking SVM can compete with the standard linear SVM and shows a slightly better performance for some targets, especially on the data sets for dual-target activity.Figure 6

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