iATC-NFMLP: Identifying Classes of Anatomical Therapeutic ChemicalsBased on Drug Networks, Fingerprints, and Multilayer Perceptron
Shunrong Tang, Lei Chen
Abstract
Background: The Anatomical Therapeutic Chemicals (ATC) classification system is a widely accepted drug classification system. It classifies drugs according to the organ or system in which they can operate and their therapeutic, pharmacological, and chemical properties. Assigning drugs into 14 classes in the first level of the system is an essential step to understanding drug properties. Several multi-label classifiers have been proposed to identify drug classes. Although their performance was good, most classifiers directly only adopted drug relationships or the features derived from these relationships, but the essential properties of drugs were not directly employed. Thus, classifiers still have a space for improvement. Objective: The aim of this study was to build a novel and powerful multilabel classifier for identifying classes in the first level of the ATC classification system for given drugs Methods: A powerful multi-label classifier, namely, iATC-NFMLP, was proposed. Two feature types were adopted to encode each drug. The first type was derived from drug relationships via a network embedding algorithm, whereas the second one represented the fingerprints of drugs. Multilayer perceptron using sigmoid as the activating function was used to learn these features for the construction of the classifier. Results: The 10-fold cross-validation results indicated that a combination of the two feature types could improve the performance of the classifier. The jackknife test on the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 82.76% and 79.27%, respectively. Results: The 10-fold cross-validation results indicated that a combination of the two feature types could improve the performance of the classifier. The jackknife test on the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 82.76% and 79.27%, respectively. Conclusion: The performance of iATC-NFMLP was best compared with all previous classifiers