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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Review Article

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

Author(s): Karim Abbasi, Parvin Razzaghi, Antti Poso, Saber Ghanbari-Ara and Ali Masoudi-Nejad*

Volume 28, Issue 11, 2021

Published on: 07 September, 2020

Page: [2100 - 2113] Pages: 14

DOI: 10.2174/0929867327666200907141016

Price: $65

Abstract

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.

Keywords: Drug-target interaction prediction, Deep learning, Machine learning, Drug discovery, DTIs prediction approaches, EC50.

« Previous Next »
[1]
Masoudi-Nejad, A.; Mousavian, Z.; Bozorgmehr, J.H. Drug-target and disease networks: polypharmacology in the post-genomic era. In Silico Pharmacol., 2013, 1(1), 17.
[http://dx.doi.org/10.1186/2193-9616-1-17] [PMID: 25505661]
[2]
Masoudi-Sobhanzadeh, Y.; Omidi, Y.; Amanlou, M.; Masoudi-Nejad, A. Drug databases and their contributions to drug repurposing. Genomics, 2020, 112(2), 1087-1095.
[http://dx.doi.org/10.1016/j.ygeno.2019.06.021] [PMID: 31226485]
[3]
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]
[4]
Goodfellow, I.; Bengio, Y.; Courville, A. Deep learning., MIT Press, 2016, 22(4), 351-354. https://doi.org/10.4258/hir.2016.22.4.351.
[5]
Zou, J.; Huss, M.; Abid, A.; Mohammadi, P.; Torkamani, A.; Telenti, A. A primer on deep learning in genomics. Nat. Genet., 2019, 51(1), 12-18.
[http://dx.doi.org/10.1038/s41588-018-0295-5] [PMID: 30478442]
[6]
Amin, N.; McGrath, A.; Chen, Y.P.P. Evaluation of deep learning in non-coding RNA classification. Nat. Machine Intelligence,, 2019, 1(5), 246.
[http://dx.doi.org/10.1038/s42256-019-0051-2]
[7]
Asgari, E.; Münch, P.C.; Lesker, T.R.; McHardy, A.C.; Mofrad, M.R.K. DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection. Bioinformatics, 2019, 35(14), 2498-2500.
[http://dx.doi.org/10.1093/bioinformatics/bty954] [PMID: 30500871]
[8]
Asgari, E.; Poerner, N.; McHardy, A.; Mofrad, M. DeepPrime2Sec: deep learning for protein secondary structure prediction from the primary sequences. bioRxiv, 2019, 705426
[http://dx.doi.org/10.1101/705426]
[9]
Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv., 2018, 4(7)eaap7885
[http://dx.doi.org/10.1126/sciadv.aap7885] [PMID: 30050984]
[10]
Min, S.; Lee, B.; Yoon, S. Deep learning in bioinformatics. Brief. Bioinform., 2017, 18(5), 851-869.
[http://dx.doi.org/10.1093/bib/bbw068]] [PMID: 27473064]
[11]
Hooshmand, S.A.; Jamalkandi, S.A.; Alavi, S.M.; Masoudi-Nejad, A. Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network. Mol. Divers., 2020. Epub ahead of print
[http://dx.doi.org/10.1007/s11030-020-10065-7] [PMID: 32193758]
[12]
Masoudi-Sobhanzadeh, Y.; Motieghader, H.; Masoudi-Nejad, A. FeatureSelect: a software for feature selection based on machine learning approaches. BMC Bioinformatics, 2019, 20(1), 170.
[http://dx.doi.org/10.1186/s12859-019-2754-0] [PMID: 30943889]
[13]
Voulodimos, A.; Doulamis, N.; Doulamis, A.; Protopapadakis, E. Deep learning for computer vision: a brief review. Comput. Intell. Neurosci., 2018, 20187068349
[http://dx.doi.org/10.1155/2018/7068349] [PMID: 29487619]
[14]
Young, T.; Hazarika, D.; Poria, S.; Cambria, E. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag., 2018, 13(3), 55-75.
[http://dx.doi.org/10.1109/MCI.2018.2840738]
[15]
McCann, B.; Bradbury, J.; Xiong, C.; Socher, R. In: Learned in translation: contextualized word vectors, NeurIPS Proceedings; Advances in Neural Information Processing Systems30 (NIPS 2017), 2017, 6294-6305..
[16]
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]
[17]
He, T.; Heidemeyer, M.; Ban, F.; Cherkasov, A.; Ester, M. SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J. Cheminform., 2017, 9(1), 24.
[http://dx.doi.org/10.1186/s13321-017-0209-z] [PMID: 29086119]
[18]
Razzaghi, P.; Abbasi, K.; Bayat, P. Learning spatial hierarchies of high-level features in deep neural network. J. Vis. Commun. Image Represent., 2020.
[http://dx.doi.org/10.1016/j.jvcir.2020.102817]
[19]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[http://dx.doi.org/10.1021/ci100050t] [PMID: 20426451]
[20]
Ma, J.; Sheridan, R.P.; Liaw, A.; Dahl, G.E.; Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model., 2015, 55(2), 263-274.
[http://dx.doi.org/10.1021/ci500747n] [PMID: 25635324]
[21]
Chakravarti, S.K.; Alla, S.R.M. Descriptor free QSAR modeling using deep learning with long short-term memory neural networks; Frontiers in Artificial Intelligence, 2019, p. 2.
[http://dx.doi.org/ 10.3389/frai.2019.00017]
[22]
Durrant, J.D.; McCammon, J.A. BINANA: a novel algorithm for ligand-binding characterization. J. Mol. Graph. Model., 2011, 29(6), 888-893.
[http://dx.doi.org/10.1016/j.jmgm.2011.01.004] [PMID: 21310640]
[23]
Rupp, M.; Tkatchenko, A.; Müller, K.R.; von Lilienfeld, O.A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 2012, 108(5)058301
[http://dx.doi.org/10.1103/PhysRevLett.108.058301] [PMID: 22400967]
[24]
Wu, Z.; Ramsundar, B.; Feinberg, E.N.; Gomes, J.; Geniesse, C.; Pappu, A.S.; Leswing, K.; Pande, V. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. (Camb.), 2017, 9(2), 513-530.
[http://dx.doi.org/10.1039/C7SC02664A] [PMID: 29629118]
[25]
Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett., 2007, 98(14)146401
[http://dx.doi.org/10.1103/PhysRevLett.98.146401] [PMID: 17501293]
[26]
Durrant, J.D.; McCammon, J.A. NNScore 2.0: a neural-network receptor-ligand scoring function. J. Chem. Inf. Model., 2011, 51(11), 2897-2903.
[http://dx.doi.org/10.1021/ci2003889] [PMID: 22017367]
[27]
Da, C.; Kireev, D. Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J. Chem. Inf. Model., 2014, 54(9), 2555-2561.
[http://dx.doi.org/10.1021/ci500319f] [PMID: 25116840]
[28]
Zeng, H.; Edwards, M.D.; Liu, G.; Gifford, D.K. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics, 2016, 32(12), i121-i127.
[http://dx.doi.org/10.1093/bioinformatics/btw255] [PMID: 27307608]
[29]
Lanchantin, J.; Singh, R.; Wang, B.; Qi, Y. Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks. Pac. Symp. Biocomput., 2017, 22, 254-265.
[http://dx.doi.org/10.1142/9789813207813_0025] [PMID: 27896980]
[30]
Alipanahi, B.; Delong, A.; Weirauch, M.T.; Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 2015, 33(8), 831-838.
[http://dx.doi.org/10.1038/nbt.3300] [PMID: 26213851]
[31]
Tsubaki, M.; Tomii, K.; Sese, J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 2019, 35(2), 309-318.
[http://dx.doi.org/10.1093/bioinformatics/bty535] [PMID: 29982330]
[32]
Öztürk, H.; Özgür, A.; Ozkirimli, E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 2018, 34(17), i821-i829.
[http://dx.doi.org/10.1093/bioinformatics/bty593] [PMID: 30423097]
[33]
Pham, H.N.; Le, T.H. Attention-based multi-input deep learning architecture for biological activity prediction: an application in EGFR inhibitors; IEEE Xplore, 2019, pp. 1-9.
[http://dx.doi.org/10.1109/KSE.2019.8919265 ]
[34]
Roy, K.; Kar, S.; Das, R.N. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment, 1st ed; Elsevier Academic Press, 2015.
[35]
Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des., 2016, 30(8), 595-608.
[http://dx.doi.org/10.1007/s10822-016-9938-8] [PMID: 27558503]
[36]
Duvenaud, D. In: Convolutional networks on graphs for learning molecular fingerprints, NeurIPS Proceedings; Advances in Neural Information Processing Systems28 (NIPS 2015), 2015, pp. 2224-2232..
[37]
Misra, I.; Shrivastava, A.; Gupta, A.; Hebert, M. In: Crossstitch networks for multi-task learning, . IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA2016, pp. 3994-4003.
[http://dx.doi.org/10.1109/CVPR.2016.433]
[38]
Bruna, J.; Zaremba, W.; Szlam, A.; LeCun, Y. Spectral networks and locally connected networks on graphs arXiv. 2013:1312-6203, 2013. Preprint Paper..
[39]
Altae-Tran, H.; Ramsundar, B.; Pappu, A.S.; Pande, V. Low data drug discovery with one-shot learning. ACS Cent. Sci., 2017, 3(4), 283-293.
[http://dx.doi.org/10.1021/acscentsci.6b00367] [PMID: 28470045]
[40]
Pope, P.; Kolouri, S.; Rostrami, M.; Martin, C.; Hoffman, H. Discovering molecular functional groups using graph convolutional neural networks. arXiv preprint arXiv:1812.00265, 2018. [Preprint paper]..
[41]
Ryu, S.; Lim, J.; Hong, S.H.; Kim, W.Y. Deeply learning molecular structure-property relationships using attentionand gate-augmented graph convolutional network arXiv preprint arXiv:1805.10988, 2018. [Preprint paper]..
[42]
Li, R.; Wang, S.; Zhu, F.; Huang, J. Adaptive graph convolutional neural networks. Thirty-Second AAAI Conference on Artificial Intelligence, 2018 , 32(1).
[43]
Gao, K.Y.; Fokoue, A.; Luo, H.; Iyengar, A.; Dey, S.; Zhang, P. In: Interpretable drug target prediction using deep neural representation,. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018, pp. 3371-3377.
[http://dx.doi.org/10.24963/ijcai.2018/468]
[44]
Pope, P.E.; Kolouri, S.; Rostami, M.; Martin, C.E.; Hoffmann, H. Explainability methods for graph convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 10772-10781.
[http://dx.doi.org/10.1109/CVPR.2019.01103]
[45]
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput., 1997, 9(8), 1735-1780.
[http://dx.doi.org/10.1162/neco.1997.9.8.1735] [PMID: 9377276]
[46]
Fooshee, D.; Mood, A.; Gutman, E.; Tavakoli, M.; Urban, G.; Liu, F.; Huynh, N.; Vrankenb, D.V.; Baldi, P. Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng., 2018, 3(3), 442-452.
[http://dx.doi.org/10.1039/C7ME00107J]
[47]
Kramer, M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChE J., 1991, 37(2), 233-243.
[http://dx.doi.org/10.1002/aic.690370209]
[48]
Karimi, M.; Wu, D.; Wang, Z.; Shen, Y. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics, 2019, 35(18), 3329-3338.
[http://dx.doi.org/10.1093/bioinformatics/btz111] [PMID: 30768156]
[49]
Sutskever, I.; Vinyals, O.; Le, Q.V. In: Sequence to sequence learning with neural networks.. NIPS’14: Proceedings of the 27th International Conference on Neural Information Processing Systems, Volume 22014, , pp. 3104-3112.
[http://dx.doi.org/10.5555/2969033.2969173]
[50]
Xu, Z.; Wang, S.; Zhu, F.; Huang, J. Seq2seq fingerprint: an unsupervised deep molecular embedding for drug discovery. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2017, pp. 285-294.
[http://dx.doi.org/ 10.1145/3107411.3107424]
[51]
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.S.; Dean, J. In: Distributed representations of words and phrases and their compositionality, NeurIPS Proceedings; , 2013, pp. 3111-3119.
[52]
Pennington, J.; Socher, R.; Manning, C. Glove: Global vectors for word representation. in conference on empirical methods in natural language processing; EMNLP, 2014, pp. 1532-1543.
[http://dx.doi.org/10.3115/v1/d14-1162 ]
[53]
Howard, J.; Ruder, S. .Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018. [Preprint paper]..
[54]
Asgari, E.; McHardy, A.C.; Mofrad, M.R.K. Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX). Sci. Rep., 2019, 9(1), 3577.
[http://dx.doi.org/10.1038/s41598-019-38746-w] [PMID: 30837494]
[55]
Asgari, E.; Mofrad, M.R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS One, 2015, 10(11)e0141287
[http://dx.doi.org/10.1371/journal.pone.0141287] [PMID: 26555596]
[56]
Özçelik, R.; Öztürk, H.; Özgür, A.; Ozkirimli, E. ChemBoost: a chemical language based approach for protein-ligand interaction prediction. Mol. Inform., 2020.
[http://dx.doi.org/10.1002/minf.202000212]] [PMID: 33225594]
[57]
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]
[58]
Bahdanau, D.; Cho, K.; Bengio, Y. In: Neural machine translation by jointly learning to align and translate, International Conference on Learning Representations (ICLR), 2015.
[59]
Hassan, M. M.; Mogollón, D. C.; Fuentes, O.; Sirimulla, S. DLSCORE: a deep learning model for predicting protein-ligand binding affinities, 2018.
[http://dx.doi.org/10.26434/chemrxiv.6159143.v1]
[60]
Abbasi, K.; Razzaghi, P.; Poso, A.; Amanlou, M.; Ghasemi, J.B.; Masoudi-Nejad, A. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics, 2020, 36(17), 4633-4642.
[http://dx.doi.org/10.1093/bioinformatics/btaa544] [PMID: 32462178]
[61]
Liu, H.; Sun, J.; Guan, J.; Zheng, J.; Zhou, S. Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics, 2015, 31(12), i221-i229.
[http://dx.doi.org/10.1093/bioinformatics/btv256] [PMID: 26072486]
[62]
Feng, Q.; Dueva, E.; Cherkasov, A.; Ester, M. A deep learning- based framework for drug-target interaction prediction arXiv preprint arXiv:1807.09741, 2018. Preprint Paper..
[63]
Shin, B.; Park, S.; Kang, K.; Ho, J.C. Self-attention based molecule representation for predicting drug-target interaction. Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106, 2019, 230-248..
[64]
Tang, J.; Szwajda, A.; Shakyawar, S.; Xu, T.; Hintsanen, P.; Wennerberg, K.; Aittokallio, T. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J. Chem. Inf. Model., 2014, 54(3), 735-743.
[http://dx.doi.org/10.1021/ci400709d] [PMID: 24521231]
[65]
Chen, X.; Ren, B.; Chen, M.; Liu, M.X.; Ren, W.; Wang, Q.X.; Zhang, L.X.; Yan, G.Y. ASDCD: antifungal synergistic drug combination database. PLoS One, 2014, 9(1)e86499
[http://dx.doi.org/10.1371/journal.pone.0086499] [PMID: 24475134]
[66]
Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res., 2016, 44(D1), D380-D384.
[http://dx.doi.org/10.1093/nar/gkv1277] [PMID: 26590256]
[67]
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]
[68]
Davis, M.I.; Hunt, J.P.; Herrgard, S.; Ciceri, P.; Wodicka, L.M.; Pallares, G.; Hocker, M.; Treiber, D.K.; Zarrinkar, P.P. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol., 2011, 29(11), 1046-1051.
[http://dx.doi.org/10.1038/nbt.1990] [PMID: 22037378]
[69]
Metz, J.T.; Johnson, E.F.; Soni, N.B.; Merta, P.J.; Kifle, L.; Hajduk, P.J. Navigating the kinome. Nat. Chem. Biol., 2011, 7(4), 200-202.
[http://dx.doi.org/10.1038/nchembio.530] [PMID: 21336281]
[70]
Richard, A.M.; Judson, R.S.; Houck, K.A.; Grulke, C.M.; Volarath, P.; Thillainadarajah, I.; Yang, C.; Rathman, J.; Martin, M.T.; Wambaugh, J.F.; Knudsen, T.B.; Kancherla, J.; Mansouri, K.; Patlewicz, G.; Williams, A.J.; Little, S.B.; Crofton, K.M.; Thomas, R.S. ToxCast chemical landscape: paving the road to 21st-century toxicology. Chem. Res. Toxicol., 2016, 29(8), 1225-1251.
[http://dx.doi.org/10.1021/acs.chemrestox.6b00135] [PMID: 27367298]
[71]
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]
[72]
Wang, T.; Qiao, Y.; Ding, W.; Mao, W.; Zhou, Y.; Gong, H. Improved fragment sampling for ab initio protein structure prediction using deep neural networks. Nature Machine Intelligence, 2019, 1(8), 347-355.
[http://dx.doi.org/10.1038/s42256-019-0075-7]
[73]
Long, M.; Zhu, H.; Wang, J.; Jordan, M.I. Deep transfer learning with joint adaptation networks. International Conference on Machine Learning, 2017, pp. 2208-2217.
[74]
Razzaghi, P. Self-taught support vector machines. Knowl. Inf. Syst., 2019, 59(3), 685-709.
[http://dx.doi.org/10.1007/s10115-018-1218-6]
[75]
Razzaghi, P.; Razzaghi, P.; Abbasi, K. Transfer subspace learning via low-rank and discriminative reconstruction matrix. Knowl. Base. Syst., 2019, 163, 174-185.
[http://dx.doi.org/10.1016/j.knosys.2018.08.026]
[76]
Abbasi, K.; Poso, A.; Ghasemi, J.; Amanlou, M.; Masoudi-Nejad, A. Deep transferable compound representation across domains and tasks for low data drug discovery. J. Chem. Inf. Model., 2019, 59(11), 4528-4539.
[http://dx.doi.org/10.1021/acs.jcim.9b00626] [PMID: 31661955]
[77]
Chadha, A.; Andreopoulos, Y. Improving adversarial discriminative domain adaptation arXiv preprint arXiv:1809.03625, 2018. [Preprint paper].
[78]
Ball, N.; Cronin, M.T.; Shen, J.; Blackburn, K.; Booth, E.D.; Bouhifd, M.; Donley, E.; Egnash, L.; Hastings, C.; Juberg, D.R.; Kleensang, A.; Kleinstreuer, N.; Kroese, E.D.; Lee, A.C.; Luechtefeld, T.; Maertens, A.; Marty, S.; Naciff, J.M.; Palmer, J.; Pamies, D.; Penman, M.; Richarz, A.N.; Russo, D.P.; Stuard, S.B.; Patlewicz, G.; van Ravenzwaay, B.; Wu, S.; Zhu, H.; Hartung, T. Toward good read-across practice (GRAP) guidance. ALTEX, 2016, 33(2), 149-166.
[http://dx.doi.org/10.14573/altex.1601251] [PMID: 26863606]
[79]
Chen, L.; Chu, C.; Lu, J.; Kong, X.; Huang, T.; Cai, Y.D. Gene ontology and KEGG pathway enrichment analysis of a drug target-based classification system. PLoS One, 2015, 10(5)e0126492
[http://dx.doi.org/10.1371/journal.pone.0126492] [PMID: 25951454]

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