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
Review Article

Application of Deep Learning Neural Networks in Computer-Aided Drug Discovery: A Review

Author(s): Jay Shree Mathivanan, Victor Violet Dhayabaran, Mary Rajathei David, Muthugobal Bagayalakshmi Karuna Nidhi, Karuppasamy Muthuvel Prasath* and Suvaiyarasan Suvaithenamudhan*

Volume 19, Issue 9, 2024

Published on: 23 January, 2024

Page: [851 - 858] Pages: 8

DOI: 10.2174/0115748936276510231123121404

Price: $65

Abstract

Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.

« Previous Next »
[1]
Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616(7958): 673-85.
[http://dx.doi.org/10.1038/s41586-023-05905-z] [PMID: 37100941]
[2]
Sabe VT, Ntombela T, Jhamba LA, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224: 113705.
[http://dx.doi.org/10.1016/j.ejmech.2021.113705] [PMID: 34303871]
[3]
Shu-Feng Z, Wei-Zhu Z. Drug design and discovery: Principles and applications. Molecules 2017; 279.
[http://dx.doi.org/10.3390/molecules22020279]
[4]
Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine learning methods in drug discovery. Molecules 2020; 25(22): 5277.
[http://dx.doi.org/10.3390/molecules25225277]
[5]
Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today 2018; 23(8): 1538-46.
[http://dx.doi.org/10.1016/j.drudis.2018.05.010] [PMID: 29750902]
[6]
Talevi A, Morales JF, Hather G, et al. Machine learning in drug discovery and development part 1: A primer. CPT Pharmacometrics Syst Pharmacol 2020; 9(3): 129-42.
[http://dx.doi.org/10.1002/psp4.12491] [PMID: 31905263]
[7]
Gertrudes JC, Maltarollo VG, Silva RA, Oliveira PR, Honório KM, da Silva ABF. Machine learning techniques and drug design. Curr Med Chem 2012; 19(25): 4289-97.
[http://dx.doi.org/10.2174/092986712802884259] [PMID: 22830342]
[8]
Agarwal S, Dugar D, Sengupta S. Ranking chemical structures for drug discovery: A new machine learning approach. J Chem Inf Model 2010; 50(5): 716-31.
[http://dx.doi.org/10.1021/ci9003865] [PMID: 20387860]
[9]
Rodrigues T, Bernardes GJL. Machine learning for target discovery in drug development. Curr Opin Chem Biol 2020; 56: 16-22.
[http://dx.doi.org/10.1016/j.cbpa.2019.10.003] [PMID: 31734566]
[10]
Gao D, Chen Q, Zeng Y, Jiang M, Zhang Y. Applications of machine learning in drug target discovery. Curr Drug Metab 2020; 21(10): 790-803.
[http://dx.doi.org/10.2174/1567201817999200728142023] [PMID: 32723266]
[11]
Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18(6): 463-77.
[http://dx.doi.org/10.1038/s41573-019-0024-5] [PMID: 30976107]
[12]
Zoffmann Sannah, , et al. Machine learning-powered antibiotics phenotypic drug discovery. Sci Rep 2019; 9(1): 5013.
[http://dx.doi.org/10.1038/s41598-019-39387-9]
[13]
Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019; 18(5): 435-41.
[http://dx.doi.org/10.1038/s41563-019-0338-z] [PMID: 31000803]
[14]
Klambauer G, Hochreiter S, Rarey M. Machine learning in drug discovery. J Chem Inf Model 2019; 59(3): 945-6.
[http://dx.doi.org/10.1021/acs.jcim.9b00136] [PMID: 30905159]
[15]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol Divers 2021; 25(3): 1315-60.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[16]
Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980; 36(4): 193-202.
[http://dx.doi.org/10.1007/BF00344251] [PMID: 7370364]
[17]
Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev 2019; 119(18): 10520-94.
[http://dx.doi.org/10.1021/acs.chemrev.8b00728] [PMID: 31294972]
[18]
Torng W, Altman RB. Graph convolutional neural networks for predicting drug-target interactions. J Chem Inf Model 2019; 59(10): 4131-49.
[http://dx.doi.org/10.1021/acs.jcim.9b00628] [PMID: 31580672]
[19]
Sarkar Chayna, , et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Inter J Mole Sci 2023; p. 2026.
[http://dx.doi.org/10.3390/ijms24032026]
[20]
Altalib Mohammed Khaldoon, Salim Naomie. Similarity-based virtual screen using enhanced Siamese multi-layer perceptron. Molecules 2021; 26(21): 6669.
[http://dx.doi.org/10.3390/molecules26216669]
[21]
Altalib MK, Salim N. Similarity-based virtual screen using enhanced Siamese deep learning methods. ACS Omega 2022; 7(6): 4769-86.
[http://dx.doi.org/10.1021/acsomega.1c04587] [PMID: 35187297]
[22]
Staszak M, Staszak K, Wieszczycka K, et al. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. Advan Rev 2022; 12(2): 1-8.
[http://dx.doi.org/10.1002/wcms.1568]
[23]
Schneider G. Mind and machine in drug design. Nat Mach Intell 2019; 1(3): 128-30.
[http://dx.doi.org/10.1038/s42256-019-0030-7]
[24]
Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin Drug Discov 2021; 16(9): 949-59.
[http://dx.doi.org/10.1080/17460441.2021.1909567] [PMID: 33779453]
[25]
Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK, et al. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26(1): 80-93.
[http://dx.doi.org/10.1016/j.drudis.2020.10.010]
[26]
Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today 2019; 24(3): 773-80.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[27]
Sperduti A, Starita A. Supervised neural networks for the classification of structures. IEEE Trans Neural Netw 1997; 8(3): 714-35.
[http://dx.doi.org/10.1109/72.572108] [PMID: 18255672]
[28]
Gori M, Monfardini G, Scarselli F. A new model for learning in graph domains. 2005 IEEE International Joint Conference on Neural Networks. IEEE, Vol. 2, 2005
[http://dx.doi.org/ 10.1109/IJCNN.2005.1555942]
[29]
Scarselli F, Gori M, Hagenbuchner M, Monfardini G, Monfardini G. The graph neural network model. IEEE Trans Neural Netw 2009; 20(1): 61-80.
[http://dx.doi.org/10.1109/TNN.2008.2005605] [PMID: 19068426]
[30]
Gallicchio C, Micheli A. Graph Echo State Networks,"The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain 2010; pp. 1-8.
[http://dx.doi.org/ 10.1109/IJCNN.2010.5596796]
[31]
Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 2021; 32(1): 4-24.
[http://dx.doi.org/10.1109/TNNLS.2020.2978386] [PMID: 32217482]
[32]
Xiong J, Xiong Z, Chen K, Jiang H, Zheng M. Graph neural networks for automated de novo drug design. Drug Discov Today 2021; 26(6): 1382-93.
[http://dx.doi.org/10.1016/j.drudis.2021.02.011] [PMID: 33609779]
[33]
Zhang XM, Liang L, Liu L, Tang MJ. Graph neural networks and their current applications in bioinformatics. Front Genet 2021; 12: 690049.
[http://dx.doi.org/10.3389/fgene.2021.690049] [PMID: 34394185]
[34]
Zhou Jie, , et al. Graph neural networks: A review of methods and applications. AI Open 2020; 1: 57-81.
[http://dx.doi.org/10.1016/j.aiopen.2021.01.001]
[35]
Nt H, Maehara T. Revisiting graph neural networks: All we have is low-pass filters. arXiv 2019.
[36]
Gama F, Bruna J, Ribeiro A. Stability properties of graph neural networks. IEEE Trans Signal Process 2020; 68: 5680-95.
[http://dx.doi.org/10.1109/TSP.2020.3026980]
[37]
Dwivedi VP, et al. Benchmarking graph neural networks. 200300982 .2020;
[38]
Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: Predicting drug–target binding affinity with graph neural networks. Bioinformatics 2021; 37(8): 1140-7.
[http://dx.doi.org/10.1093/bioinformatics/btaa921] [PMID: 33119053]
[39]
Yang Z, Zhong W, Zhao L, Yu-Chian Chen C. MGraphDTA: Deep multiscale graph neural network for explainable drug–target binding affinity prediction. Chem Sci 2022; 13(3): 816-33.
[http://dx.doi.org/10.1039/D1SC05180F] [PMID: 35173947]
[40]
Wang K, Zhou R, Tang J, et al. GraphscoreDTA: Optimized graph neural network for protein–ligand binding affinity prediction. Bioinformatics 2023; 39(6): btad340.
[http://dx.doi.org/10.1093/bioinformatics/btad340]
[41]
Zhang X, Gao H, Wang H, et al. PLANET: A multi-objective graph neural network model for protein–ligand binding affinity prediction. J Chem Inf Model 2024; 64(7): 2205-20.
[http://dx.doi.org/10.1021/acs.jcim.3c00253] [PMID: 37319418]
[42]
He H, Chen G, Chen CYC. NHGNN-DTA: A node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction. Bioinformatics 2023; 39(6): btad355.
[http://dx.doi.org/10.1093/bioinformatics/btad355] [PMID: 37252835]
[43]
Liao J, Chen H, Wei L, Wei L. GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information. Comput Biol Med 2022; 150: 106145.
[http://dx.doi.org/10.1016/j.compbiomed.2022.106145]
[44]
Tian Q, Ding M, Yang H, et al. Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks. Comb Chem High Throughput Screen 2022; 25(4): 634-41.
[http://dx.doi.org/10.2174/1386207324666210215101825] [PMID: 33588722]
[45]
Verma S, Zhang Z-L. Stability and generalization of graph convolutional neural networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining July 2019 Pages. 1539–1548.
[http://dx.doi.org/10.1145/3292500.3330956]
[46]
James A, Towsley D. Diffusion-convolutional neural networks. Adv Neural Inform Process Sys 2016; p. 29.
[47]
Dernbach S. Quantum walk neural networks for graph-structured data. In: Complex networks and their applications vii: volume 2 proceedings the 7th international conference on complex networks and their applications complex networks 2018 7. Springer International Publishing, 2019.
[http://dx.doi.org/10.1007/978-3-030-05414-4_15]
[48]
Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints. Adv Neural Inf Process Sys 2015; p. 28.
[49]
Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks 160902907 . 2016
[50]
Li R, Wang S, Zhu F, et al. Adaptive graph convolutional neural networks. Proc Conf AAAI Artif Intell 2018; 32(1)
[51]
Puy G, Kitic S, Pérez P. Unifying local and non-local signal processing with graph cnns. 170207759.2017;
[52]
Verma S, Zhang Z-L. Graph capsule convolutional neural networks. 180508090 . 2018
[53]
Miyuki S, Nagayasu K, Shibul H, et al. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci Rep 2021; 11(1): 525.
[http://dx.doi.org/10.1038/s41598-020-80113-7]
[54]
Gomes J, Ramsundar B, Feinberg EN, Pande VS, et al. Atomic convolutional networks for predicting protein-ligand binding affinity. arXiv:170310603. 2017
[55]
Son Jeongtae, , Kim Dongsup. . Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PLoS One 2021; 16(4): e0249404.
[http://dx.doi.org/10.1371/journal.pone.0249404]
[56]
Chen J, Si YW, Un CW, Siu SWI. Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network. J Cheminform 2021; 13(1): 93.
[http://dx.doi.org/10.1186/s13321-021-00570-8] [PMID: 34838140]
[57]
Zhang S, Tong H, Xu J, Maciejewski R. Graph convolutional networks: a comprehensive review. Comput Soc Netw 2019; 6(1): 1-23.
[58]
Pope PE, Kolouri S, Rostami M, Martin CE and, Hoffmann H. Explainability methods for graph convolutional neural networks. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition CA, USA, 2019; 10764.10773.
[http://dx.doi.org/10.1109/CVPR.2019.01103]
[59]
Monti F, Boscaini D, Masci J, Rodolà E, Svoboda J, Bronstein MM. "Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs,&quot 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA. 2017; p. 5425-5434. doi: 10.1109/CVPR.2017.576
[http://dx.doi.org/10.1109/CVPR.2017.576]
[60]
Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 2016; 29.
[61]
Sun M, Zhao S, Gilvary C, Elemento O, Zhou J, Wang F. Graph convolutional networks for computational drug development and discovery. Brief Bioinform 2020; 21(3): 919-35.
[http://dx.doi.org/10.1093/bib/bbz042] [PMID: 31155636]
[62]
Shishir FS, Hasib KM, Sakib S, Maitra S, Shah FM. "De Novo Drug Property Prediction using Graph Convolutional Neural Networks," 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India 2021; 01-6.
[http://dx.doi.org/10.1109/R10-HTC53172.2021.9641611]
[63]
Shen Huimin, et al. A Cascade graph convolutional network for predicting protein–ligand binding affinity. Int J Mol Sci 2021; 22(8): 4023.
[http://dx.doi.org/10.3390/ijms22084023]
[64]
Mukherjee S, Ghosh M, Basuchowdhuri P. DeepGLSTM: deep graph convolutional network and LSTM based approach for predicting drug-target binding affinity. In: Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) Society for Industrial and Applied Mathematics 2022.
[http://dx.doi.org/10.1137/1.9781611977172.82]
[65]
Moesser MA, et al. Protein-ligand interaction graphs: Learning from ligand-shaped 3d interaction graphs to improve binding affinity prediction. bioRxiv 2022.
[http://dx.doi.org/10.1101/2022.03.04.483012]
[66]
Haiping Z, Mani SK. DeepBindGCN: Integrating molecular vector representation with graph convolutional neural networks for protein–ligand interaction prediction. Molecules 2023; 4691.
[67]
Zheng L, Fan J, Mu Y. Onionnet: a multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction. ACS Omega 2019; 4(14): 15956-65.
[http://dx.doi.org/10.1021/acsomega.9b01997] [PMID: 31592466]
[68]
Jin Yuan , et al. EmbedDTI: Enhancing the molecular representations via sequence embedding and graph convolutional network for the prediction of drug-target interaction. Biomolecules 2021; 17: 83.
[http://dx.doi.org/10.3390/biom11121783]
[69]
Miyazaki Yu, et al. Comprehensive exploration of target‐specific ligands using a graph convolution neural network. Molecular informatics 2020; 39: 1-2.
[http://dx.doi.org/10.1002/minf.201900095]
[70]
Sreeraman S, Kannan MP, Singh Kushwah RB, et al. Drug design and disease diagnosis: The potential of deep learning models in biology. Curr Bioinform 2023; 18(3): 208-20.
[http://dx.doi.org/10.2174/1574893618666230227105703]
[71]
Alankrita A. l Mamta M, i Gopi B. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights 2021; 1.1:100004;
[http://dx.doi.org/10.1016/j.jjimei.2020.100004]
[72]
Gui J, Sun Z, Wen Y, Tao D, Ye J. A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans Knowl Data Eng 2023; 35(4): 3313-32.
[http://dx.doi.org/10.1109/TKDE.2021.3130191]
[73]
Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal 2019; 58: 101552.
[http://dx.doi.org/10.1016/j.media.2019.101552] [PMID: 31521965]
[74]
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM 2020; 63(11): 139-44.
[http://dx.doi.org/10.1145/3422622]
[75]
Kusiak A. Convolutional and generative adversarial neural networks in manufacturing. Int J Prod Res 2020; 58(5): 1594-604.
[http://dx.doi.org/10.1080/00207543.2019.1662133]
[76]
Kao PY, Yang YC, Chiang WY, et al. Exploring the advantages of quantum generative adversarial networks in Generative Chemistry. J Chem Inf Model 2023; 63(11): 3307-18.
[http://dx.doi.org/10.1021/acs.jcim.3c00562] [PMID: 37171372]
[77]
Batool Maria, , Ahmad Bilal, , Choi Sangdun. . A structure-based drug discovery paradigm. Int J Mol Sci 2019; 20(11): 2783.
[http://dx.doi.org/10.3390/ijms20112783]
[78]
Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol Pharm 2017; 14(9): 3098-104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]
[79]
Patel V, Shah M. Artificial intelligence and machine learning in drug discovery and development. Intelligent Medicine 2022; 2(3): 134-40.
[http://dx.doi.org/10.1016/j.imed.2021.10.001]
[80]
Li J, Topaloglu RO, Ghosh S. Quantum generative models for small molecule drug discovery. IEEE Trans Quantum Eng 2021; 2: 1-8.
[http://dx.doi.org/10.1109/TQE.2021.3104804]
[81]
Lin Eugene Lin, Chieh-Hsin Lane, Hsien-Yuan . Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design. Molecules 2020; 25(14): 3250.
[http://dx.doi.org/10.3390/molecules25143250]
[82]
Goodfellow I, Poget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst 2014; 27.
[83]
Tripathi S, Augustin AI, Dunlop A, et al. Recent Advances and Application of Generative Adversarial Networks in Drug Discovery, Development, and Targeting. Artificial Intelligence in the life Sciences 2022; 100045.
[84]
Polykovskiy D, Zhebrak A, Vetrov D, et al. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm 2018; 15(10): 4398-405.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00839] [PMID: 30180591]
[85]
Luo S, Guan J, Ma J. Peng j. A 3D generative model for structure-based drug design. Adv Neural Inf Process Syst 2021; 34: 6229-39.
[86]
Rifaioglu AS, Cetin Atalay R, Cansen Kahraman D, Doğan T, Martin M, Atalay V. MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. Bioinformatics 2021; 37(5): 693-704.
[http://dx.doi.org/10.1093/bioinformatics/btaa858] [PMID: 33067636]
[87]
Shi W, Singha M, Srivastava G, Pu L, Ramanujam J, Brylinski M. Pocket2Drug: An encoder-decoder deep neural network for the target-based drug design. Front Pharmacol 2022; 13: 837715.
[http://dx.doi.org/10.3389/fphar.2022.837715] [PMID: 35359869]
[88]
Liu Ke, Sun X, Jia L. Chemi-Net: A molecular graph convolutional network for accurate drug property prediction. Int J Mol Sci 2019; 20(14): 3389.
[http://dx.doi.org/10.3390/ijms20143389]
[89]
Wallach I, Dzamba M, Heifets A. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. 151002855 . 2015
[90]
Fernández-Llaneza D, Ulander S, Gogishvili D, Nittinger E, Zhao H, Tyrchan C. Siamese Recurrent neural network with a self-attention mechanism for bioactivity prediction. ACS Omega 2021; 6(16): 11086-94.
[http://dx.doi.org/10.1021/acsomega.1c01266] [PMID: 34056263]
[91]
Xu M, et al. Deepgan: Generating molecule for drug discovery based on generative adversarial network. 2021 IEEE Symposium on Computers and Communications (ISCC). Athens, Greece. 2021; pp. 1-6.
[http://dx.doi.org/ 10.1109/ISCC53001.2021.9631396]
[92]
Mukesh K, Venkata SI, Cherreddy S. EA, Oriya IR. A Variational Autoencoder—General Adversarial Networks (VAE-GAN) Based Model for Ligand Designing. International Conference on Innovative Computing and Communications: Proceedings of ICICC 2022. Singapore. Volume 1 2022
[93]
Yu Zhouxin, et al. Predicting drug–disease associations through layer attention graph convolutional network. Brief Bioinform 2021; 22(4): 243.
[http://dx.doi.org/10.1093/bib/bbaa243]
[94]
Elbasani E, Njimbouom SN, Oh TJ, Kim EH, Lee H, Kim JD. GCRNN: Graph convolutional recurrent neural network for compound-protein interaction prediction. BMC Bioinformatics 2022; 22(5) (Suppl. 5): 616.
[PMID: 35016607]

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