Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

iATC-NFMLP: Identifying Classes of Anatomical Therapeutic Chemicals Based on Drug Networks, Fingerprints, and Multilayer Perceptron

Author(s): Shunrong Tang and Lei Chen*

Volume 17, Issue 9, 2022

Published on: 30 June, 2022

Page: [814 - 824] Pages: 11

DOI: 10.2174/1574893617666220318093000

Price: $65

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.

Conclusion: The performance of iATC-NFMLP was best compared with all previous classifiers.

Keywords: Drug, ATC classification system, multi-label classification, network embedding algorithm, fingerprint, multilayer perceptron.

« Previous Next »
Graphical Abstract

[1]
Niu B, Lu Y, Wang J, et al. 2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors. Comput Struct Biotechnol J 2018; 17: 39-48.
[http://dx.doi.org/10.1016/j.csbj.2018.11.007] [PMID: 30595814]
[2]
Zhao J, Xu P, Liu X, Ji X, Li M, Dev S, et al. Application of machine learning methods for the development of antidiabetic drugs. Curr Pharm Des 2022; 28(4): 260-71.
[PMID: 34161205]
[3]
Xie J, Liang R, Wang Y, Huang J, Cao X, Niu B. Progress in target drug molecules for alzheimer’s disease. Curr Top Med Chem 2020; 20(1): 4-36.
[http://dx.doi.org/10.2174/1568026619666191203113745] [PMID: 31797761]
[4]
Hu Y, Lu Y, Wang S, Zhang M, Qu X, Niu B. Application of machine learning approaches for the design and study of anticancer drugs. Curr Drug Targets 2019; 20(5): 488-500.
[http://dx.doi.org/10.2174/1389450119666180809122244] [PMID: 30091413]
[5]
Niu B, Zhao M, Su Q, et al. 2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors. Mol Divers 2017; 21(2): 413-26.
[http://dx.doi.org/10.1007/s11030-017-9732-0] [PMID: 28275924]
[6]
Zhao M, Wang L, Zheng L, et al. 2D-QSAR and 3D-QSAR analyses for EGFR inhibitors. Biomed Res Int 2017; 2017: 4649191.
[http://dx.doi.org/10.1155/2017/4649191] [PMID: 28630865]
[7]
Chen L, Zeng WM, Cai YD, Feng KY, Chou KC. Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One 2012; 7(4): e35254.
[http://dx.doi.org/10.1371/journal.pone.0035254] [PMID: 22514724]
[8]
Chen L, Lu J, Zhang N, Huang T, Cai YD. A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes. Mol Biosyst 2014; 10(4): 868-77.
[http://dx.doi.org/10.1039/c3mb70490d] [PMID: 24492783]
[9]
Cheng X, Zhao SG, Xiao X, Chou KC. iATC-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 2017; 33(3): 341-6.
[http://dx.doi.org/10.1093/bioinformatics/btx387] [PMID: 28172617]
[10]
Cheng X, Zhao SG, Xiao X, Chou KC. iATC-mHyb: A hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget 2017; 8(35): 58494-503.
[http://dx.doi.org/10.18632/oncotarget.17028] [PMID: 28938573]
[11]
Nanni L, Brahnam S. Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound. Bioinformatics 2017; 33(18): 2837-41.
[http://dx.doi.org/10.1093/bioinformatics/btx278] [PMID: 28444139]
[12]
Zhou JP, Chen L, Guo ZH. iATC-NRAKEL: An efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs. Bioinformatics 2020; 36(5): 1391-6.
[PMID: 31593226]
[13]
Zhou JP, Chen L, Wang T, Liu M. iATC-FRAKEL: A simple multi-label web server for recognizing anatomical therapeutic chemical classes of drugs with their fingerprints only. Bioinformatics 2020; 36(11): 3568-9.
[http://dx.doi.org/10.1093/bioinformatics/btaa166] [PMID: 32154836]
[14]
Wang X, Wang Y, Xu Z, Xiong Y, Wei DQ. ATC-NLSP: Prediction of the classes of anatomical therapeutic chemicals using a network-based label space partition method. Front Pharmacol 2019; 10: 971.
[http://dx.doi.org/10.3389/fphar.2019.00971] [PMID: 31543820]
[15]
Lu Z, Chou KC. iATC_Deep-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals by deep learning. Adv Biosci Biotechnol 2020; 11(5): 153-9.
[http://dx.doi.org/10.4236/abb.2020.115012]
[16]
Lumini A, Nanni L. Convolutional neural networks for ATC classification. Curr Pharm Des 2018; 24(34): 4007-12.
[http://dx.doi.org/10.2174/1381612824666181112113438] [PMID: 30417778]
[17]
Zhao H, Li Y, Wang J. A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals. Bioinformatics 2021; 37(18): 2841-7.
[http://dx.doi.org/10.1093/bioinformatics/btab204] [PMID: 33769479]
[18]
Olson T, Singh R. Predicting anatomic therapeutic chemical classification codes using tiered learning. BMC Bioinformatics 2017; 18(S8): 266.
[http://dx.doi.org/10.1186/s12859-017-1660-6] [PMID: 28617230]
[19]
Liu Z, Guo F, Gu J, et al. Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources. Bioinformatics 2015; 31(11): 1788-95.
[http://dx.doi.org/10.1093/bioinformatics/btv055] [PMID: 25638810]
[20]
Wang YC, Chen SL, Deng NY, Wang Y. Network predicting drug’s anatomical therapeutic chemical code. Bioinformatics 2013; 29(10): 1317-24.
[http://dx.doi.org/10.1093/bioinformatics/btt158] [PMID: 23564845]
[21]
Chen FS, Jiang ZR. Prediction of drug’s Anatomical Therapeutic Chemical (ATC) code by integrating drug-domain network. J Biomed Inform 2015; 58: 80-8.
[http://dx.doi.org/10.1016/j.jbi.2015.09.016] [PMID: 26434987]
[22]
Kuhn M, Szklarczyk D, Pletscher-Frankild S, et al. STITCH 4: Integration of protein-chemical interactions with user data. Nucleic Acids Res 2014; 42(D1): D401-7.
[http://dx.doi.org/10.1093/nar/gkt1207] [PMID: 24293645]
[23]
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017; 45(D1): D353-61.
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[24]
Hattori M, Tanaka N, Kanehisa M, Goto S. SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses. Nucleic Acids Res 2010; 38 (Suppl. 2): W652-6.
[http://dx.doi.org/10.1093/nar/gkq367] [PMID: 20460463]
[25]
Cho H, Berger B, Peng J. Compact integration of multi-network topology for functional analysis of genes. Cell Syst 2016; 3(6): 540-8.
[http://dx.doi.org/10.1016/j.cels.2016.10.017] [PMID: 27889536]
[26]
Köhler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 2008; 82(4): 949-58.
[http://dx.doi.org/10.1016/j.ajhg.2008.02.013] [PMID: 18371930]
[27]
Tong H, Faloutsos C, Pan J, Eds. Fast random walk with restart and its applications. IEEE Sixth International Conference on Data Mining (ICDM’06). 2006 Dec 18-22; Hong Kong, China. 2006; pp. 613-22.
[http://dx.doi.org/10.1109/ICDM.2006.70]
[28]
Zhang W, Liu F, Luo L, Zhang J. Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics 2015; 16(1): 365.
[http://dx.doi.org/10.1186/s12859-015-0774-y] [PMID: 26537615]
[29]
Ding YJ, Tang JJ, Guo F. Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing 2019; 325: 211-24.
[http://dx.doi.org/10.1016/j.neucom.2018.10.028]
[30]
Ding Y, Tang J, Guo F. Identification of drug-side effect association via semisupervised model and multiple kernel learning. IEEE J Biomed Health Inform 2019; 23(6): 2619-32.
[http://dx.doi.org/10.1109/JBHI.2018.2883834] [PMID: 30507518]
[31]
Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Model 1988; 28(1): 31-6.
[http://dx.doi.org/10.1021/ci00057a005]
[32]
Landrum G. RDKit: Open-source cheminformatics 2006. Available from: http://www.rdkit.org
[33]
Tsoumakas G, Katakis I. Multi-label classification: An overview. Int J Data Warehous Min 2007; 3(3): 1-13.
[http://dx.doi.org/10.4018/jdwm.2007070101]
[34]
Riedmiller M. Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms. Comput Stand Interfaces 1994; 16(3): 265-78.
[http://dx.doi.org/10.1016/0920-5489(94)90017-5]
[35]
Maxwell A, Li R, Yang B, et al. Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinformatics 2017; 18(S14): 523.
[http://dx.doi.org/10.1186/s12859-017-1898-z] [PMID: 29297288]
[36]
Abadi M, Ed. TensorFlow: Learning functions at scale. Proc 21st ACM SIGPLAN Int Conf Funct Program 2016. 2016; 1.
[http://dx.doi.org/10.1145/2951913.2976746]
[37]
Sua JN, Lim SY, Yulius MH, et al. Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites. Chemometr Intell Lab 2020; 2020: 206.
[38]
Kohavi R, Ed. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc 14th Int Joint Conf Artif Intell. 1995; 14(2): 1137-45.
[39]
Tsoumakas G, Vlahavas I, Eds. Random k-Labelsets: An Ensemble Method for Multilabel Classification. Berlin, Heidelberg: Springer 2007.
[40]
Zhang ML, Li YK, Liu XY, Geng X. Binary relevance for multi-label learning: An overview. Front Comput Sci 2018; 12(2): 191-202.
[http://dx.doi.org/10.1007/s11704-017-7031-7]
[41]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[42]
Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[43]
Chen Z, Zhao P, Li F, et al. iLearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief Bioinform 2020; 21(3): 1047-57.
[http://dx.doi.org/10.1093/bib/bbz041] [PMID: 31067315]
[44]
Zhang YH, Li H, Zeng T, et al. Identifying transcriptomic signatures and rules for SARS-CoV-2 infection. Front Cell Dev Biol 2021; 8: 627302.
[http://dx.doi.org/10.3389/fcell.2020.627302] [PMID: 33505977]
[45]
Li F, Li C, Wang M, et al. GlycoMine: A machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome. Bioinformatics 2015; 31(9): 1411-9.
[http://dx.doi.org/10.1093/bioinformatics/btu852] [PMID: 25568279]
[46]
Song J, Wang Y, Li F, et al. iProt-Sub: A comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2019; 20(2): 638-58.
[http://dx.doi.org/10.1093/bib/bby028] [PMID: 29897410]
[47]
Jia Y, Zhao R, Chen L. Similarity-based machine learning model for predicting the metabolic pathways of compounds. IEEE Access 2020; 8: 130687-96.
[http://dx.doi.org/10.1109/ACCESS.2020.3009439]
[48]
Zhao X, Chen L, Lu J. A similarity-based method for prediction of drug side effects with heterogeneous information. Math Biosci 2018; 306: 136-44.
[http://dx.doi.org/10.1016/j.mbs.2018.09.010] [PMID: 30296417]
[49]
Read J, Reutemann P, Pfahringer B, Holmes G. MEKA: A Multi-label/Multi-target Extension to WEKA. J Mach Learn Res 2016; 17.
[50]
Li GZ, Yan SX, You M, Sun S, Ou A. Intelligent ZHENG classification of hypertension depending on ML-kNN and information fusion. Evid Based Complement Alternat Med 2012; 2012: 837245.
[http://dx.doi.org/10.1155/2012/837245] [PMID: 22701510]
[51]
Lee CP, Lin CJ. Large-scale linear rankSVM. Neural Comput 2014; 26(4): 781-817.
[http://dx.doi.org/10.1162/NECO_a_00571] [PMID: 24479776]

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy