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.
authors.affiliation: UMIT, Division for Bioinformatics and Translational Research, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria.
authors.forename: Matthias
authors.initials: M
authors.lastname: Dehmer
authors.affiliation: 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.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.affiliation: UMIT, Division for Bioinformatics and Translational Research, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria.
authors.forename: Shailesh
authors.initials: S
authors.lastname: Tripathi
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D008958:Models, Molecular; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012984:Software