title: Consensual classification of drug and nondrug compounds.
abstract: A special consensual approach is discussed for separating a molecular group with a proven pharmacological activity from another molecular group without any activity. It is mainly a group decision to produce a consensus of multiple classification results obtained with a single classification algorithm. For this purpose, the constructed model has a preprocessing unit which consists of transformation of input patterns by random matrices and median filtering to generate independent errors for a single type of classifier and postprocessing for consensus. The neural network based consensus classifier operating with MOE descriptors was applied to a set of 641 chemical structures. The confirmed drugs were classified with an accuracy of 86.54% while nondrugs resulted in 82.67% accuracy.
authors.affiliation: Computer Engineering Department, Istanbul Kultur University, Atakoy, Istanbul, Bakirkoy, 34156, Turkey. a.pehlivanli@iku.edu.tr
authors.forename: Ayca C
authors.initials: AC
authors.lastname: Pehlivanli
authors.forename: Turgay
authors.initials: T
authors.lastname: Ibrikci
authors.forename: Okan K
authors.initials: OK
authors.lastname: Ersoy
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D015195:Drug Design; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000145:classification*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations