Note on naive Bayes based on binary descriptors in cheminformatics.
A plethora of articles on naive Bayes classifiers, where the chemical compounds to be classified are represented by binary-valued (absent or present type) descriptors, have appeared in the cheminformatics literature over the past decade. The principal goal of this paper is to describe how a naive Bayes classifier based on binary descriptors (NBCBBD) can be employed as a feature selector in an efficient manner suitable for cheminformatics. In the process, we point out a fact well documented in other disciplines that NBCBBD is a linear classifier and is therefore intrinsically suboptimal for classifying compounds that are nonlinearly separable in their binary descriptor space. We investigate the performance of the proposed algorithm on classifying a subset of the MDDR data set, a standard molecular benchmark data set, into active and inactive compounds.
Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Joe A
JA
Townsend
Robert C
RC
Glen
Hamse Y
HY
Mussa
D000465:Algorithms; D001499:Bayes Theorem; D001688:Biological Products / Q000737:chemistry* / Q000494:pharmacology; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D006801:Humans; D048088:Informatics; D008958:Models, Molecular; D013329:Structure-Activity Relationship
D001688:Biological Products; D004791:Enzyme Inhibitors