title: Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns.
abstract: The emerging chemical patterns (ECP) approach has been introduced for compound classification. Thus far, only very few ECP applications have been reported. Here, we further investigate the ECP methodology by studying complex classification problems. The analysis involves multi-target data sets with systematically organized subsets of compounds having distinct or overlapping target activities and, in addition, data sets containing classes of specifically active compounds with different mechanism-of-action. In systematic classification trials focusing on individual compound subsets or mechanistic classes, ECP calculations utilizing numerical descriptors achieve moderate to high sensitivity, dependent on the data set, and consistently high specificity. Accurate ECP predictions are already obtained on the basis of very small learning sets with only three positive training instances, which distinguishes the ECP approach from many other machine learning techniques.
authors.affiliation: Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn, Germany.
authors.forename: Vigneshwaran
authors.initials: V
authors.lastname: Namasivayam
authors.forename: Ye
authors.initials: Y
authors.lastname: Hu
authors.forename: Jenny
authors.initials: J
authors.lastname: Balfer
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D001185:Artificial Intelligence; D062313:Databases, Pharmaceutical; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification*; D010600:Pharmacology; D043562:Receptors, G-Protein-Coupled / Q000819:agonists / Q000037:antagonists & inhibitors
chemical_list: D004364:Pharmaceutical Preparations; D043562:Receptors, G-Protein-Coupled