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


Cite item

Full Text

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.

About the authors

Jay Mathivanan

Department of Bioinformatics, Bishop Heber College (Autonomous)

Email: info@benthamscience.net

Victor Dhayabaran

P.G. and Research Department of Chemistry,, Bishop Heber College (Autonomous)

Email: info@benthamscience.net

Mary David

Department of Bioinformatics, Bishop Heber College (Autonomous)

Email: info@benthamscience.net

Muthugobal Karuna Nidhi

Department of Mathematics, Tamilavel Umamaheswaranar Karanthai Arts College

Email: info@benthamscience.net

Karuppasamy Prasath

Department of Biotechnology, Ayya Nadar Janaki Ammal College

Author for correspondence.
Email: info@benthamscience.net

Suvaiyarasan Suvaithenamudhan

Department of Bioinformatics,, Bishop Heber College (Autonomous)

Author for correspondence.
Email: info@benthamscience.net

References

  1. Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616(7958): 673-85. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.3390/ijms24032026
  20. Altalib Mohammed Khaldoon, Salim Naomie. Similarity-based virtual screen using enhanced Siamese multi-layer perceptron. Molecules 2021; 26(21): 6669. doi: 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. doi: 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. doi: 10.1002/wcms.1568
  23. Schneider G. Mind and machine in drug design. Nat Mach Intell 2019; 1(3): 128-30. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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 doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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,' 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA. 2017; p. 5425-5434. doi: 10.1109/CVPR.2017.576 doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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; doi: 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. doi: 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. doi: 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. doi: 10.1145/3422622
  75. Kusiak A. Convolutional and generative adversarial neural networks in manufacturing. Int J Prod Res 2020; 58(5): 1594-604. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers