Large-scale evaluation of molecular descriptors by means of clustering

Matthias Dehmer; Frank Emmert-Streib; Shailesh Tripathi

doi:10.1371/journal.pone.0083956

Large-scale evaluation of molecular descriptors by means of clustering

Matthias Dehmer
, Frank Emmert-Streib
, Shailesh Tripathi

Private University for Health Sciences, Medical Informatics and Technology
Queen's University Belfast

Research output: Contribution to journal › Article › peer-review

Abstract

Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively.

Original language	English
Article number	e83956
Journal	PLoS ONE
Volume	8
Issue number	12
DOIs	https://doi.org/10.1371/journal.pone.0083956
Publication status	Published - 31 Dec 2013
Externally published	Yes

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title = "Large-scale evaluation of molecular descriptors by means of clustering",

abstract = "Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively.",

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AB - Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively.

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Large-scale evaluation of molecular descriptors by means of clustering

Abstract

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