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

PLoS One. 2013 Dec 31;8(12):e83956. doi: 10.1371/journal.pone.0083956. eCollection 2013.

Authors

Matthias Dehmer¹, Frank Emmert-Streib², Shailesh Tripathi¹

Affiliations

¹ UMIT, Division for Bioinformatics and Translational Research, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria.
² Queen's University Belfast, Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Belfast, United Kingdom.

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.

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Cluster Analysis*
Humans
Models, Molecular
Molecular Structure
Quantitative Structure-Activity Relationship*
Software*

Grants and funding

Matthias Dehmer and Shailesh Tripathi thank the Austrian Science Funds for supporting this work (project P22029-N13). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.