A Comparative Analytical Review on Machine Learning Methods in Drugtarget
Interactions Prediction

Zahra      Nikraftar; Mohammad   Reza   Keyvanpour
doi:10.2174/1573409919666230111164340
Abstract

Background: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predicting drug-target interactions have been introduced successfully to solve these problems and have received extensive attention.
Objective: In this paper, we provided a summary of databases that are useful in DTI prediction and intend to concentrate on machine learning methods as a chemogenomic approach in drug discovery. Unlike previous surveys, we propose a comparative analytical framework based on the evaluation criteria.
Methods: In our suggested framework, there are three stages to follow: First, we present a comprehensive categorization of machine learning-based techniques as a chemogenomic approach for drug-target interaction prediction problems; Second, to evaluate the proposed classification, several general criteria are provided; Third, unlike other surveys, according to the evaluation criteria introduced in the previous stage, a comparative analytical evaluation is performed for each approach.
Results: This systematic research covers the earliest, most recent, and outstanding techniques in the DTI prediction problem and identifies the advantages and weaknesses of each approach separately. Additionally, it can be helpful in the effective selection and improvement of DTI prediction techniques, which is the main superiority of the proposed framework.
Conclusion: This paper gives a thorough overview to serve as a guide and reference for other researchers by providing an analytical framework which can help to select, compare, and improve DTI prediction methods.
Keywords: Drug-target Interactions (DTIs), drug discovery, machine learning methods, computational techniques, chemogenomic approach, comparative analytical framework.
[1]
Yella, J.; Yaddanapudi, S.; Wang, Y.; Jegga, A. Changing Trends in Computational Drug Repositioning. Pharmaceuticals,  2018, 11(2), 57. Epub ahead of print
 [http://dx.doi.org/10.3390/ph11020057] [PMID: 29874824]

[2]
Li, J.; Zheng, S.; Chen, B.; Butte, A.J.; Swamidass, S.J.; Lu, Z. A survey of current trends in computational drug repositioning. Brief. Bioinform.,  2016, 17(1), 2-12.
 [http://dx.doi.org/10.1093/bib/bbv020] [PMID: 25832646]

[3]
Dotolo, S.; Marabotti, A.; Facchiano, A.; Tagliaferri, R. A review on drug repurposing applicable to COVID-19. Brief. Bioinform.,  2021, 22(2), 726-741.
 [http://dx.doi.org/10.1093/bib/bbaa288] [PMID: 33147623]

[4]
Alaimo, S.; Pulvirenti, A. Network-based drug repositioning: approaches, resources, and research directions. In: Vanhaelen, Q. (eds) Computational Methods for Drug Repurposing. Methods in Molecular Biology, vol 1903. Humana Press, New York, NY 2019.
 [http://dx.doi.org/10.1007/978-1-4939-8955-3_6]

[5]
Xue, H.; Li, J.; Xie, H.; Wang, Y. Review of drug repositioning approaches and resources. Int. J. Biol. Sci.,  2018, 14(10), 1232-1244.
 [http://dx.doi.org/10.7150/ijbs.24612] [PMID: 30123072]

[6]
Shim, J.S.; Liu, J.O. Recent advances in drug repositioning for the discovery of new anticancer drugs. Int. J. Biol. Sci.,  2014, 10(7), 654-663.
 [http://dx.doi.org/10.7150/ijbs.9224] [PMID: 25013375]

[7]
Meng, F.R.; You, Z.H.; Chen, X.; Zhou, Y.; An, J.Y. Prediction of drug–target interaction networks from the integration of protein sequences and drug chemical structures. Molecules,  2017, 22(7), 1119.
 [http://dx.doi.org/10.3390/molecules22071119] [PMID: 28678206]

[8]
Bahi, M.; Batouche, M. Drug-target interaction prediction in drug repositioning based on deep semi-supervised learning. In: Computational Intelligence and Its Applications. CIIA 2018. IFIP Advances in Information and Communication Technology; Amine, A.; Mouhoub, M.; Ait Mohamed, O.; Djebbar, B., Eds.; Springer: Cham, 2018, 522.
 [http://dx.doi.org/10.1007/978-3-319-89743-1_27]

[9]
Agamah, F.E.; Mazandu, G.K.; Hassan, R.; Bope, C.D.; Thomford, N.E.; Ghansah, A.; Chimusa, E.R. Computational/in silico methods in drug target and lead prediction. Brief. Bioinform.,  2020, 21(5), 1663-1675.
 [http://dx.doi.org/10.1093/bib/bbz103] [PMID: 31711157]

[10]
March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Chen, H.; Rastelli, G. On the integration of in silico drug design methods for drug repurposing. Front. Pharmacol.,  2017, 8, 298.
 [http://dx.doi.org/10.3389/fphar.2017.00298] [PMID: 28588497]

[11]
Yamanishi, Y. Chemogenomic Approaches to Infer Drug--Target Interaction Networks.Data Mining for Systems Biology: Methods and Protocols; Mamitsuka, H.; DeLisi, C.; Kanehisa, M., Eds , 97-113.
 [http://dx.doi.org/10.1007/978-1-62703-107-3_9]

[12]
Li, Y.Y.; An, J.; Jones, S.J.M. A computational approach to finding novel targets for existing drugs. PLOS Comput. Biol.,  2011, 7(9), e1002139.
 [http://dx.doi.org/10.1371/journal.pcbi.1002139] [PMID: 21909252]

[13]
Chu, Y.; Kaushik, A.C.; Wang, X.; Wang, W.; Zhang, Y.; Shan, X.; Salahub, D.R.; Xiong, Y.; Wei, D.Q. DTI-CDF: A cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief. Bioinform.,  2021, 22(1), 451-462.
 [http://dx.doi.org/10.1093/bib/bbz152] [PMID: 31885041]

[14]
Xuan, P.; Fan, M.; Cui, H.; Zhang, T.; Nakaguchi, T. GVDTI: graph convolutional and variational autoencoders with attributelevel attention for drug-protein interaction prediction. Brief. Bioinform.,  2022, 23(1), bbab453.
 [http://dx.doi.org/10.1093/bib/bbab453] [PMID: 34718408]

[15]
Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics,  2008, 24(13), i232-i240.
 [http://dx.doi.org/10.1093/bioinformatics/btn162] [PMID: 18586719]

[16]
Jung, Y.S.; Kim, Y.; Cho, Y.R. Comparative analysis of networkbased approaches and machine learning algorithms for predicting drug-target interactions. Methods,  2022, 198, 19-31.
 [http://dx.doi.org/10.1016/j.ymeth.2021.10.007] [PMID: 34737033]

[17]
Keyvanpour, M.R.; Haddadi, F.; Mehrmolaei, S. DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Comput. Biol. Chem.,  2022, 99107707.
 [http://dx.doi.org/10.1016/j.compbiolchem.2022.107707] [PMID: 35691227]

[18]
Zhang, W.; Lin, W.; Zhang, D.; Wang, S.; Shi, J.; Niu, Y. Recent advances in the machine learning-based drug-target interaction prediction. Curr. Drug Metab.,  2019, 20(3), 194-202.
 [http://dx.doi.org/10.2174/1389200219666180821094047] [PMID: 30129407]

[19]
Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. A review of network-based approaches to drug repositioning. Brief. Bioinform.,  2018, 19(5), 878-892.
 [http://dx.doi.org/10.1093/bib/bbx017] [PMID: 28334136]

[20]
Ding, H.; Takigawa, I.; Mamitsuka, H.; Zhu, S. Similarity-based machine learning methods for predicting drug-target interactions: A brief review. Brief. Bioinform.,  2014, 15(5), 734-747.
 [http://dx.doi.org/10.1093/bib/bbt056] [PMID: 23933754]

[21]
Mousavian, Z.; Masoudi-Nejad, A. Drug-target interaction prediction via chemogenomic space: Learning-based methods. Expert Opin. Drug Metab. Toxicol.,  2014, 10(9), 1273-1287.
 [http://dx.doi.org/10.1517/17425255.2014.950222] [PMID: 25112457]

[22]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug–target interaction prediction: Databases, web servers and computational models. Brief. Bioinform.,  2016, 17(4), 696-712.
 [http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]

[23]
Sachdev, K.; Gupta, M.K. A comprehensive review of feature based methods for drug target interaction prediction. J. Biomed. Inform.,  2019, 93103159.
 [http://dx.doi.org/10.1016/j.jbi.2019.103159] [PMID: 30926470]

[24]
Chen, R.; Liu, X.; Jin, S.; Lin, J.; Liu, J. Machine learning for drugtarget interaction prediction. Molecules,  2018, 23(9), 2208.
 [http://dx.doi.org/10.3390/molecules23092208] [PMID: 30200333]

[25]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Computational prediction of drug-target interactions using chemogenomic approaches: An empirical survey. Brief. Bioinform.,  2019, 20(4), 1337-1357.
 [http://dx.doi.org/10.1093/bib/bby002] [PMID: 29377981]

[26]
Bagherian, M.; Sabeti, E.; Wang, K.; Sartor, M.A. Nikolovska- Coleska, Z.; Najarian, K. Machine learning approaches and databases for prediction of drug-target interaction: A survey paper. Brief. Bioinform.,  2021, 22(1), 247-269.
 [http://dx.doi.org/10.1093/bib/bbz157] [PMID: 31950972]

[27]
Haddadi, F.; Kayvanpour, M.R. DTIP: A comparative analytical framework for chemogenomic drugtarget interactions prediction. Curr. Computeraided Drug Des.,  2021, 17(1), 2-21.
 [http://dx.doi.org/10.2174/1573409916666191218124520] [PMID: 31854276]

[28]
Lim, S.; Lu, Y.; Cho, C.Y.; Sung, I.; Kim, J.; Kim, Y.; Park, S.; Kim, S. A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput. Struct. Biotechnol. J.,  2021, 19, 1541-1556.
 [http://dx.doi.org/10.1016/j.csbj.2021.03.004] [PMID: 33841755]

[29]
Fakhraei, S.; Huang, B.; Raschid, L.; Getoor, L. Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2014, 11(5), 775-787.
 [http://dx.doi.org/10.1109/TCBB.2014.2325031] [PMID: 26356852]

[30]
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model.,  1988, 28(1), 31-36.
 [http://dx.doi.org/10.1021/ci00057a005]

[31]
Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res.,  2008, 36(Database issue)(Suppl. 1), D901-D906.
 [http://dx.doi.org/10.1093/nar/gkm958] [PMID: 18048412]

[32]
Günther, S.; Kuhn, M.; Dunkel, M.; Campillos, M.; Senger, C.; Petsalaki, E.; Ahmed, J.; Urdiales, E.G.; Gewiess, A.; Jensen, L.J.; Schneider, R.; Skoblo, R.; Russell, R.B.; Bourne, P.E.; Bork, P.; Preissner, R. SuperTarget and Matador: Resources for exploring drug-target relationships. Nucleic Acids Res.,  2008, 36(Database issue), D919-D922.
 [PMID: 17942422]

[33]
Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res.,  2008, 36(Database issue), D684-D688.
 [PMID: 18084021]

[34]
Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J. Cibrián-Uhalte, E.; Davies, M.; Dedman, N.; Karlsson, A.; Magariños, M.P.; Overington, J.P.; Papadatos, G.; Smit, I.; Leach, A.R. The ChEMBL database in 2017. Nucleic Acids Res.,  2017, 45(D1), D945-D954.
 [http://dx.doi.org/10.1093/nar/gkw1074] [PMID: 27899562]

[35]
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res.,  2018, 46(D1), D1074-D1082.
 [http://dx.doi.org/10.1093/nar/gkx1037] [PMID: 29126136]

[36]
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-D361.
 [http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]

[37]
Kanehisa, M.; Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res.,  2000, 28(1), 27-30.
 [http://dx.doi.org/10.1093/nar/28.1.27] [PMID: 10592173]

[38]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res.,  2016, 44(D1), D1045-D1053.
 [http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]

[39]
Schomburg, I.; Chang, A.; Ebeling, C.; Gremse, M.; Heldt, C.; Huhn, G.; Schomburg, D. BRENDA, the enzyme database: Updates and major new developments. Nucleic Acids Res.,  2004, 32(90001), 431D-433.
 [http://dx.doi.org/10.1093/nar/gkh081] [PMID: 14681450]

[40]
Davis, A.P.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; McMorran, R.; Wiegers, J.; Wiegers, T.C.; Mattingly, C.J. The comparative toxicogenomics database: Update 2019. Nucleic Acids Res.,  2019, 47(D1), D948-D954.
 [http://dx.doi.org/10.1093/nar/gky868] [PMID: 30247620]

[41]
Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic target database. Nucleic Acids Res.,  2002, 30(1), 412-415.
 [http://dx.doi.org/10.1093/nar/30.1.412] [PMID: 11752352]

[42]
von Eichborn, J.; Murgueitio, M.S.; Dunkel, M.; Koerner, S.; Bourne, P.E.; Preissner, R. PROMISCUOUS: a database for network- based drug-repositioning. Nucleic Acids Res.,  2011, 39(Database), D1060-D1066.
 [http://dx.doi.org/10.1093/nar/gkq1037] [PMID: 21071407]

[43]
Kringelum, J.; Kjaerulff, S.K.; Brunak, S.; Lund, O.; Oprea, T.I.; Taboureau, O. ChemProt-3.0: A global chemical biology diseases mapping. Database (Oxford),  2016, 2016bav123.
 [http://dx.doi.org/10.1093/database/bav123] [PMID: 26876982]

[44]
Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res.,  2014, 42(D1), D401-D407.
 [http://dx.doi.org/10.1093/nar/gkt1207] [PMID: 24293645]

[45]
Kuhn, M.; Letunic, I.; Jensen, L.J.; Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res.,  2016, 44(D1), D1075-D1079.
 [http://dx.doi.org/10.1093/nar/gkv1075] [PMID: 26481350]

[46]
Wang, R.; Fang, X.; Lu, Y.; Yang, C.Y.; Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem.,  2005, 48(12), 4111-4119.
 [http://dx.doi.org/10.1021/jm048957q] [PMID: 15943484]

[47]
Consortium, U. UniProt: A hub for protein information. Nucleic Acids Res.,  2015, 43(D1), D204-D212.
 [http://dx.doi.org/10.1093/nar/gku989] [PMID: 25348405]

[48]
Okuno, Y.; Yang, J.; Taneishi, K.; Yabuuchi, H.; Tsujimoto, G. GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res.,  2006, 34(90001), D673-D677.
 [http://dx.doi.org/10.1093/nar/gkj028] [PMID: 16381956]

[49]
Finn, R.D.; Bateman, A.; Clements, J.; Coggill, P.; Eberhardt, R.Y.; Eddy, S.R.; Heger, A.; Hetherington, K.; Holm, L.; Mistry, J.; Sonnhammer, E.L.L.; Tate, J.; Punta, M. Pfam: The protein families database. Nucleic Acids Res.,  2014, 42(D1), D222-D230.
 [http://dx.doi.org/10.1093/nar/gkt1223] [PMID: 24288371]

[50]
Magariños, M.P.; Carmona, S.J.; Crowther, G.J.; Ralph, S.A.; Roos, D.S.; Shanmugam, D.; Van Voorhis, W.C.; Agüero, F. TDR Targets: a chemogenomics resource for neglected diseases. Nucleic Acids Res.,  2012, 40(D1), D1118-D1127.
 [http://dx.doi.org/10.1093/nar/gkr1053] [PMID: 22116064]

[51]
Gao, Z.; Li, H.; Zhang, H.; Liu, X.; Kang, L.; Luo, X.; Zhu, W.; Chen, K.; Wang, X.; Jiang, H. PDTD: A web-accessible protein database for drug target identification. BMC Bioinformatics,  2008, 9(1), 104.
 [http://dx.doi.org/10.1186/1471-2105-9-104] [PMID: 18282303]

[52]
Nath, A.; Kumari, P.; Chaube, R. Prediction of human drug targets and their interactions using machine learning methods: Current and future perspectives. Computational Drug Discovery and Design; Gore, M; Jagtap, U.B., Ed.; 21-30.
 [http://dx.doi.org/10.1007/978-1-4939-7756-7_2]

[53]
Yu, W.; Yan, Y.; Liu, Q.; Wang, J.; Jiang, Z. Predicting drug-target interaction networks of human diseases based on multiple feature information. Pharmacogenomics,  2013, 14(14), 1701-1707.
 [http://dx.doi.org/10.2217/pgs.13.162] [PMID: 24192119]

[54]
Zhang, Z.; Chen, L.; Zhong, F.; Wang, D.; Jiang, J.; Zhang, S.; Jiang, H.; Zheng, M.; Li, X. Graph neural network approaches for drug-target interactions. Curr. Opin. Struct. Biol.,  2022, 73102327.
 [http://dx.doi.org/10.1016/j.sbi.2021.102327] [PMID: 35074533]

[55]
Yu, J.; Guo, M.; Needham, C.J.; Huang, Y.; Cai, L.; Westhead, D.R. Simple sequence-based kernels do not predict protein-protein interactions. Bioinformatics,  2010, 26(20), 2610-2614.
 [http://dx.doi.org/10.1093/bioinformatics/btq483] [PMID: 20801913]

[56]
Chawla, N.; Bowyer, K.W.; Hall, L.O. SMOTE: synthetic minority over-sampling technique. J. Intel. Res.,  2002, 16, 321-357.

[57]
Neethu, M.S.; Rajasree, R. Sentiment analysis in twitter using machine learning techniques. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT); IEEE: Tiruchengode, India, 2013, pp. 1-5.
 [http://dx.doi.org/10.1109/ICCCNT.2013.6726818]

[58]
Crawford, M.; Khoshgoftaar, T.M.; Prusa, J.D.; Richter, A.N.; Al Najada, H. Survey of review spam detection using machine learning techniques. J. Big Data,  2015, 2(1), 23.
 [http://dx.doi.org/10.1186/s40537-015-0029-9]

[59]
Islam, M.M.; Haque, M.R.; Iqbal, H.; Hasan, M.M.; Hasan, M.; Kabir, M.N. Breast cancer prediction: A comparative study using machine learning techniques. SN Computer Science,  2020, 1(5), 290.
 [http://dx.doi.org/10.1007/s42979-020-00305-w]

[60]
Verde, L.; De Pietro, G.; Sannino, G. Voice disorder identification by using machine learning techniques. IEEE Access,  2018, 6, 16246-16255.
 [http://dx.doi.org/10.1109/ACCESS.2018.2816338]

[61]
Haddadi, F.; Keyvanpour, M. PULBLM: A computational positiveunlabeled learning method for drug-target interactions prediction. In. 10th Information and Knowledge Technology Conference (ICIKT2019),  2019, pp. 1-5.

[62]
Sadeghi, S.S.; Keyvanpour, M.R. An analytical review of computational drug repurposing. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2021, 18(2), 472-488.
 [http://dx.doi.org/10.1109/TCBB.2019.2933825] [PMID: 31403439]

[63]
Xia, Z.; Wu, L.Y.; Zhou, X.; Wong, S.T.C. Semi-supervised drugprotein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol.,  2010, 4(S2)(Suppl. 2), S6.
 [http://dx.doi.org/10.1186/1752-0509-4-S2-S6] [PMID: 20840733]

[64]
Hattori, M.; Okuno, Y.; Goto, S.; Kanehisa, M. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc.,  2003, 125(39), 11853-11865.
 [http://dx.doi.org/10.1021/ja036030u] [PMID: 14505407]

[65]
Luo, H.; Li, M.; Yang, M.; Wu, F.X.; Li, Y.; Wang, J. Biomedical data and computational models for drug repositioning: A comprehensive review. Brief. Bioinform.,  2021, 22(2), 1604-1619.
 [http://dx.doi.org/10.1093/bib/bbz176] [PMID: 32043521]

[66]
Bleakley, K.; Yamanishi, Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics,  2009, 25(18), 2397-2403.
 [http://dx.doi.org/10.1093/bioinformatics/btp433] [PMID: 19605421]

[67]
Mei, J.P.; Kwoh, C.K.; Yang, P.; Li, X.L.; Zheng, J. Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics,  2013, 29(2), 238-245.
 [http://dx.doi.org/10.1093/bioinformatics/bts670] [PMID: 23162055]

[68]
Shi, J.Y.; Yiu, S.M.; Li, Y.; Leung, H.C.M.; Chin, F.Y.L. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods,  2015, 83, 98-104.
 [http://dx.doi.org/10.1016/j.ymeth.2015.04.036] [PMID: 25957673]

[69]
Zhang, W.; Chen, Y.; Li, D. Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules,  2017, 22(12), 2056.
 [http://dx.doi.org/10.3390/molecules22122056] [PMID: 29186828]

[70]
Liu, B.; Pliakos, K.; Vens, C.; Tsoumakas, G. Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery. Appl. Intell.,  2022, 52(4), 3705-3727.
 [http://dx.doi.org/10.1007/s10489-021-02495-z]

[71]
Cobanoglu, M.C.; Liu, C.; Hu, F.; Oltvai, Z.N.; Bahar, I. Predicting drug-target interactions using probabilistic matrix factorization. J. Chem. Inf. Model.,  2013, 53(12), 3399-3409.
 [http://dx.doi.org/10.1021/ci400219z] [PMID: 24289468]

[72]
Liu, Y.; Wu, M.; Miao, C.; Zhao, P.; Li, X.L. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLOS Comput. Biol.,  2016, 12(2), e1004760.
 [http://dx.doi.org/10.1371/journal.pcbi.1004760] [PMID: 26872142]

[73]
Gönen, M. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics,  2012, 28(18), 2304-2310.
 [http://dx.doi.org/10.1093/bioinformatics/bts360] [PMID: 22730431]

[74]
Ezzat, A.; Zhao, P.; Wu, M.; Li, X.L.; Kwoh, C.K. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2017, 14(3), 646-656.
 [http://dx.doi.org/10.1109/TCBB.2016.2530062] [PMID: 26890921]

[75]
Gao, L.G.; Yang, M.Y.; Wang, J.X. Collaborative matrix factorization with soft regularization for drug-target interaction prediction. J. Comput. Sci. Technol.,  2021, 36(2), 310-322.
 [http://dx.doi.org/10.1007/s11390-021-0844-8]

[76]
Ding, Y.; Tang, J.; Guo, F. Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization. Brief. Bioinform.,  2022, 23(2), 23.
 [http://dx.doi.org/10.1093/bib/bbab582]

[77]
Zhang, X.; Li, L.; Ng, M.K.; Zhang, S. Drug–target interaction prediction by integrating multiview network data. Comput. Biol. Chem.,  2017, 69, 185-193.
 [http://dx.doi.org/10.1016/j.compbiolchem.2017.03.011] [PMID: 28648470]

[78]
Li, Z.R.; Lin, H.H.; Han, L.Y.; Jiang, L.; Chen, X.; Chen, Y.Z. PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res.,  2006, 34(Web Server), W32-W37.
 [http://dx.doi.org/10.1093/nar/gkl305] [PMID: 16845018]

[79]
Mauri, A.; Consonni, V.; Pavan, M. Dragon software: An easy approach to molecular descriptor calculations. Match (Mulh.),  2006, 56, 237-248.

[80]
Tabei, Y.; Pauwels, E.; Stoven, V.; Takemoto, K.; Yamanishi, Y. Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinformatics,  2012, 28(18), i487-i494.
 [http://dx.doi.org/10.1093/bioinformatics/bts412] [PMID: 22962471]

[81]
Rao, H.B.; Zhu, F.; Yang, G.B.; Li, Z.R.; Chen, Y.Z. Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res.,  2011, 39((Web Server issue)(Suppl. 2)), W385-W390.
 [http://dx.doi.org/10.1093/nar/gkr284] [PMID: 21609959]

[82]
Shen, H.B.; Chou, K.C. PseAAC: A flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem.,  2008, 373(2), 386-388.
 [http://dx.doi.org/10.1016/j.ab.2007.10.012] [PMID: 17976365]

[83]
Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem.,  2011, 32(7), 1466-1474.
 [http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]

[84]
Ruiz-Blanco, Y.B.; Paz, W.; Green, J.; Marrero-Ponce, Y. ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins. BMC Bioinformatics,  2015, 16(1), 162.
 [http://dx.doi.org/10.1186/s12859-015-0586-0] [PMID: 25982853]

[85]
Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K.C. Pse-in- One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res.,  2015, 43(W1), W65-W71.
 [http://dx.doi.org/10.1093/nar/gkv458] [PMID: 25958395]

[86]
Cao, D.S.; Xiao, N.; Xu, Q.S.; Chen, A.F. Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions. Bioinformatics,  2015, 31(2), 279-281.
 [http://dx.doi.org/10.1093/bioinformatics/btu624] [PMID: 25246429]

[87]
Hong, H.; Xie, Q.; Ge, W.; Qian, F.; Fang, H.; Shi, L.; Su, Z.; Perkins, R.; Tong, W. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model.,  2008, 48(7), 1337-1344.
 [http://dx.doi.org/10.1021/ci800038f] [PMID: 18564836]

[88]
Dong, J.; Cao, D.S.; Miao, H.Y.; Liu, S.; Deng, B.C.; Yun, Y.H.; Wang, N.N.; Lu, A.P.; Zeng, W.B.; Chen, A.F. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J. Cheminform.,  2015, 7(1), 60.
 [http://dx.doi.org/10.1186/s13321-015-0109-z] [PMID: 26664458]

[89]
Li, Z.R.; Han, L.Y.; Xue, Y.; Yap, C.W.; Li, H.; Jiang, L.; Chen, Y.Z. MODEL-molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds. Biotechnol. Bioeng.,  2007, 97(2), 389-396.
 [http://dx.doi.org/10.1002/bit.21214] [PMID: 17013940]

[90]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform.,  2011, 3(1), 33.
 [http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]

[91]
van den Berg, B.A.; Reinders, M.J.T.; Roubos, J.A.; Ridder, D. SPiCE: A web-based tool for sequence-based protein classification and exploration. BMC Bioinformatics,  2014, 15(1), 93.
 [http://dx.doi.org/10.1186/1471-2105-15-93] [PMID: 24685258]

[92]
Cao, D.S.; Xu, Q.S.; Hu, Q.N.; Liang, Y.Z. ChemoPy: Freely available python package for computational biology and chemoinformatics. Bioinformatics,  2013, 29(8), 1092-1094.
 [http://dx.doi.org/10.1093/bioinformatics/btt105] [PMID: 23493324]

[93]
Cao, D-S.; Liang, Y-Z.; Yan, J. PyDPI: Freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. J. Chem. Inf. Model.,  2013, 53(11), 3086-3096.
 [http://dx.doi.org/10.1021/ci400127q]

[94]
Hinselmann, G.; Rosenbaum, L.; Jahn, A.; Fechner, N.; Zell, A. jCompoundMapper: An open source Java library and commandline tool for chemical fingerprints. J. Cheminform.,  2011, 3(1), 3.
 [http://dx.doi.org/10.1186/1758-2946-3-3] [PMID: 21219648]

[95]
Cao, D.S.; Xu, Q.S.; Liang, Y.Z. propy: A tool to generate various modes of Chou’s PseAAC. Bioinformatics,  2013, 29(7), 960-962.
 [http://dx.doi.org/10.1093/bioinformatics/btt072] [PMID: 23426256]

[96]
Landrum, G. Rdkit documentation. Release,  2013, 1, 4.

[97]
Tabei, Y.; Yamanishi, Y. Scalable prediction of compound-protein interactions using minwise hashing. BMC Syst. Biol.,  2013, 7, (Suppl 6)(Suppl. 6), S3.
 [http://dx.doi.org/10.1186/1752-0509-7-S6-S3] [PMID: 24564870]

[98]
Mousavian, Z.; Khakabimamaghani, S.; Kavousi, K. Masoudi- Nejad, A. Drug–target interaction prediction from PSSM based evolutionary information. J. Pharmacol. Toxicol. Methods,  2016, 78, 42-51.
 [http://dx.doi.org/10.1016/j.vascn.2015.11.002] [PMID: 26592807]

[99]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci.,  2017, 418-419, 546-560.
 [http://dx.doi.org/10.1016/j.ins.2017.08.045]

[100]
Redkar, S.; Mondal, S.; Joseph, A.; Hareesha, K.S. A machine learning approach for drug-target interaction prediction using wrapper feature selection and class balancing. Mol. Inform.,  2020, 39(5), 1900062.
 [http://dx.doi.org/10.1002/minf.201900062] [PMID: 32003548]

[101]
Sharifabad, M.M.; Sheikhpour, R.; Gharaghani, S. BRNS + SSFSM-DTI: A hybrid method for drug-target interaction prediction based on balanced reliable negative samples and semisupervised feature selection. Chemom. Intell. Lab. Syst.,  2022, 220104462.
 [http://dx.doi.org/10.1016/j.chemolab.2021.104462]

[102]
Rifaioglu, A.S.; Atas, H.; Martin, M.J.; Cetin-Atalay, R.; Atalay, V. Doğan, T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief. Bioinform.,  2019, 20(5), 1878-1912.
 [http://dx.doi.org/10.1093/bib/bby061] [PMID: 30084866]

[103]
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-learning-based drug–target interaction prediction. J. Proteome Res.,  2017, 16(4), 1401-1409.
 [http://dx.doi.org/10.1021/acs.jproteome.6b00618] [PMID: 28264154]

[104]
Sarker, I.H. Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci.,  2021, 2(6), 420.
 [http://dx.doi.org/10.1007/s42979-021-00815-1] [PMID: 34426802]

[105]
Deng, L; Yu, D. Deep learning: methods and applications. Foundations and trends in signal processing,  2014, 7, 197-387.

[106]
Wang, L.; You, Z.H.; Chen, X.; Xia, S.X.; Liu, F.; Yan, X.; Zhou, Y.; Song, K.J. A computational-based method for predicting drugtarget interactions by using stacked autoencoder deep neural network. J. Comput. Biol.,  2018, 25(3), 361-373.
 [http://dx.doi.org/10.1089/cmb.2017.0135] [PMID: 28891684]

[107]
Zhao, T.; Hu, Y.; Valsdottir, L.R.; Zang, T.; Peng, J. Identifying drug–target interactions based on graph convolutional network and deep neural network. Brief. Bioinform.,  2021, 22(2), 2141-2150.
 [http://dx.doi.org/10.1093/bib/bbaa044] [PMID: 32367110]

[108]
Peng, J.; Li, J.; Shang, X. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinformatics,  2020, 21(S13)(Suppl. 13), 394.
 [http://dx.doi.org/10.1186/s12859-020-03677-1] [PMID: 32938374]

[109]
Wang, Y.B.; You, Z.H.; Yang, S.; Yi, H.C.; Chen, Z.H.; Zheng, K. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med. Inform. Decis. Mak.,  2020, 20(S2)(Suppl. 2), 49.
 [http://dx.doi.org/10.1186/s12911-020-1052-0] [PMID: 32183788]

[110]
Zhang, P.; Wei, Z.; Che, C.; Jin, B. DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug- Target interaction prediction. Comput. Biol. Med.,  2022, 142105214.
 [http://dx.doi.org/10.1016/j.compbiomed.2022.105214] [PMID: 35030496]

[111]
Chen, C.; Shi, H.; Jiang, Z.; Salhi, A.; Chen, R.; Cui, X.; Yu, B. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Comput. Biol. Med.,  2021, 136104676.
 [http://dx.doi.org/10.1016/j.compbiomed.2021.104676] [PMID: 34375902]

[112]
Wang, L.; Wong, L.; Chen, Z.H.; Hu, J.; Sun, X.F.; Li, Y.; You, Z.H. MSPEDTI: Prediction of drug-target interactions via molecular structure with protein evolutionary information. Biology (Basel),  2022, 11(5), 740.
 [http://dx.doi.org/10.3390/biology11050740] [PMID: 35625468]

[113]
Mohan, M. Ensemble learning models for drug target interaction prediction. In. 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, 09-11 May 2022, Salem, India, IEEE, , pp. 138-143.

[114]
Sachdev, K.; Gupta, M.K. Predicting drug target interactions using dimensionality reduction with ensemble learning. In: Proceedings of ICRIC 2019; Springer, 2020, pp. 79-89.
 [http://dx.doi.org/10.1007/978-3-030-29407-6_7]

[115]
Tanha, J.; Abdi, Y.; Samadi, N.; Razzaghi, N.; Asadpour, M. Boosting methods for multi-class imbalanced data classification: An experimental review. J. Big Data,  2020, 7(1), 70.
 [http://dx.doi.org/10.1186/s40537-020-00349-y]

[116]
Węgier, W.; Koziarski, M.; Woźniak, M. Multicriteria classifier ensemble learning for imbalanced data. IEEE Access,  2022, 10, 16807-16818.
 [http://dx.doi.org/10.1109/ACCESS.2022.3149914]

[117]
Yu, H.; Chen, J.; Xu, X.; Li, Y.; Zhao, H.; Fang, Y.; Li, X.; Zhou, W.; Wang, W.; Wang, Y. A systematic prediction of multiple drugtarget interactions from chemical, genomic, and pharmacological data. PLoS One,  2012, 7(5), e37608.
 [http://dx.doi.org/10.1371/journal.pone.0037608] [PMID: 22666371]

[118]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinformatics,  2016, 17(S19)(Suppl. 19), 509.
 [http://dx.doi.org/10.1186/s12859-016-1377-y] [PMID: 28155697]

[119]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods,  2017, 129, 81-88.
 [http://dx.doi.org/10.1016/j.ymeth.2017.05.016] [PMID: 28549952]

[120]
Wang, L.; You, Z.H.; Chen, X.; Yan, X.; Liu, G.; Zhang, W. Rfdt: A rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information. Curr. Protein Pept. Sci.,  2018, 19(5), 445-454.
 [http://dx.doi.org/10.2174/1389203718666161114111656] [PMID: 27842479]

[121]
Pliakos, K.; Vens, C. Drug-target interaction prediction with treeensemble learning and output space reconstruction. BMC Bioinformatics,  2020, 21(1), 49.
 [http://dx.doi.org/10.1186/s12859-020-3379-z] [PMID: 32033537]

[122]
Wang, Y.; Wang, L.; Wong, L.; Zhao, B.; Su, X.; Li, Y.; You, Z. RoFDT: Identification of drug–target interactions from protein sequence and drug molecular structure using rotation forest. Biology,  2022, 11(5), 741.
 [http://dx.doi.org/10.3390/biology11050741] [PMID: 35625469]

[123]
Ouali, Y.; Hudelot, C.; Tami, M. An overview of deep semisupervised learning. arXiv preprint, 200605278. 

[124]
Ma, T.; Xiao, C.; Zhou, J. Drug similarity integration through attentive multi-view graph auto-encoders. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence,  2018, pp. 3477-3483.
 [http://dx.doi.org/10.24963/ijcai.2018/483]

[125]
Chen, H.; Zhang, Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PLoS One,  2013, 8(5), e62975.
 [http://dx.doi.org/10.1371/journal.pone.0062975] [PMID: 23667553]

[126]
Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning. J. Biomed. Inform.,  2017, 68, 167-183.
 [http://dx.doi.org/10.1016/j.jbi.2017.03.006] [PMID: 28300647]

[127]
Thafar, M.A.; Olayan, R.S.; Albaradei, S.; Bajic, V.B.; Gojobori, T.; Essack, M.; Gao, X. DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning. J. Cheminform.,  2021, 13(1), 71.
 [http://dx.doi.org/10.1186/s13321-021-00552-w] [PMID: 34551818]

[128]
Le, D.H.; Nguyen-Ngoc, D. Drug repositioning by integrating known disease-gene and drug-target associations in a semisupervised learning model. Acta Biotheor.,  2018, 66(4), 315-331.
 [http://dx.doi.org/10.1007/s10441-018-9325-z] [PMID: 29700660]

[129]
Yu, W.; Cheng, X.; Li, Z.; Jiang, Z. Predicting drug-target interactions based on an improved semi-supervised learning approach. Drug Dev. Res.,  2011, 72(2), 219-224.
 [http://dx.doi.org/10.1002/ddr.20418]

[130]
Gu, Q.; Ding, Y.; Zhang, T. Prediction drug-target interaction networks based on semi-supervised learning method. In: 2016 35th Chinese Control Conference (CCC); IEEE: Chengdu, China, 2016.
 [http://dx.doi.org/10.1109/ChiCC.2016.7554493]

[131]
Hao, M.; Wang, Y.; Bryant, S.H. Improved prediction of drugtarget interactions using regularized least squares integrating with kernel fusion technique. Anal. Chim. Acta,  2016, 909, 41-50.
 [http://dx.doi.org/10.1016/j.aca.2016.01.014] [PMID: 26851083]

[132]
Ding, Y.; Tang, J.; Guo, F. Identification of drug–target interactions via dual laplacian regularized least squares with multiple kernel fusion. Knowl. Base. Syst.,  2020, 204106254.
 [http://dx.doi.org/10.1016/j.knosys.2020.106254]

[133]
Sulistiawan, F.; Kusuma, W.A.; Ramadhanti, N.S. Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi-Supervised Learning Model. In: 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS); IEEE: Depok, Indonesia, 2020, pp. 83-88.
 [http://dx.doi.org/10.1109/ICACSIS51025.2020.9263241]

[134]
Bahi, M.; Batouche, M. Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform. In: 2018 international conference on intelligent systems and computer vision (ISCV),02-04 April 2018; IEEE: Fez, Morocco, 2018, pp. 1-7.
 [http://dx.doi.org/10.1109/ISACV.2018.8354081]

[135]
Vihinen, M. How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC genomics; BioMed. Central, 2012, pp. 1-10.
 [http://dx.doi.org/10.1186/1471-2164-13-S4-S2]

[136]
Haghani, S.; Keyvanpour, M.R. A systemic analysis of link prediction in social network. Artif. Intell. Rev.,  2019, 52(3), 1961-1995.
 [http://dx.doi.org/10.1007/s10462-017-9590-2]

[137]
Lu, Y.; Guo, Y.; Korhonen, A. Link prediction in drug-target interactions network using similarity indices. BMC Bioinformatics,  2017, 18(1), 39.
 [http://dx.doi.org/10.1186/s12859-017-1460-z] [PMID: 28095781]

[138]
Wang, C.; Wang, W.; Lu, K.; Zhang, J.; Chen, P.; Wang, B. Predicting drug-target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition. Int. J. Mol. Sci.,  2020, 21(16), 5694.
 [http://dx.doi.org/10.3390/ijms21165694] [PMID: 32784497]

[139]
Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit.,  1997, 30(7), 1145-1159.
 [http://dx.doi.org/10.1016/S0031-3203(96)00142-2]

[140]
Pahikkala, T.; Airola, A. Pietilä S.; Shakyawar, S.; Szwajda, A.; Tang, J.; Aittokallio, T. Toward more realistic drug-target interaction predictions. Brief. Bioinform.,  2015, 16(2), 325-337.
 [http://dx.doi.org/10.1093/bib/bbu010] [PMID: 24723570]

[141]
Tanoori, B.; Zolghadri Jahromi, M.; Mansoori, E.G. Binding affinity prediction for binary drug-target interactions using semisupervised transfer learning. J. Comput. Aided Mol. Des.,  2021, 35(8), 883-900.
 [http://dx.doi.org/10.1007/s10822-021-00404-7] [PMID: 34189637]

[142]
Zhang, R. An ensemble learning approach for improving drugtarget interactions prediction. In: Proceedings of the 4th International Conference on Computer Engineering and Networks; Springer: Heidelberg, 2015, pp. 433-442.
 [http://dx.doi.org/10.1007/978-3-319-11104-9_51]

[143]
Mamitsuka, H.; Mamitsuka, H. Latent feature kernels for link prediction on sparse graphs. IEEE Trans. Neural Netw. Learn. Syst.,  2012, 23(11), 1793-1804.
 [http://dx.doi.org/10.1109/TNNLS.2012.2215337] [PMID: 24808073]

[144]
Perlman, L.; Gottlieb, A.; Atias, N.; Ruppin, E.; Sharan, R. Combining drug and gene similarity measures for drug-target elucidation. J. Comput. Biol.,  2011, 18(2), 133-145.
 [http://dx.doi.org/10.1089/cmb.2010.0213] [PMID: 21314453]

[145]
Peng, L.; Zhu, W.; Liao, B.; Duan, Y.; Chen, M.; Chen, Y.; Yang, J. Screening drug-target interactions with positive-unlabeled learning. Sci. Rep.,  2017, 7(1), 8087.
 [http://dx.doi.org/10.1038/s41598-017-08079-7] [PMID: 28808275]
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DOI https://dx.doi.org/10.2174/1573409919666230111164340	Print ISSN 1573-4099
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Current Computer-Aided Drug Design

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