Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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

A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and Applications

Author(s): Sundaravadivelu Sumathi*, Kanagaraj Suganya, Kandasamy Swathi, Balraj Sudha, Arumugam Poornima, Chalos Angel Varghese and Raghu Aswathy

Volume 29, Issue 13, 2023

Published on: 18 April, 2023

Page: [1013 - 1025] Pages: 13

DOI: 10.2174/1381612829666230412084137

Price: $65

conference banner
Abstract

It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial intelligence (AI) have helped to overcome the challenges in the drug discovery pipeline. Artificial intelligence (AI) has taken root in various pharmaceutical sectors, from lead compound identification to clinical trials. Deep learning (DL) is a component of artificial intelligence (AI) that has excelled in many fields of Artificial intelligence (AI) research over the past decades. Its numerous applications in the realms of science and technology, especially in biomedicine and bioinformatics, are witnessed deep learning (DL) applications significantly accelerate drug discovery and pharmaceutical research in recent years, and their usefulness has exceeded expectations and shown good promise in tackling a range of issues with drug discovery. Deep learning (DL) holds great potential for drug development since it allows for sophisticated image interpretation, molecular structure and function prediction, and the automated creation of novel chemical entities with specific features. In the process of drug discovery, deep learning (DL) can be incorporated at all stages like identification of targets, prognostic biomarkers, drug designing and development, synergism and antagonism prediction, etc. This review summarizes various approaches of deep learning (DL) in drug discovery like deep generative models for drug discovery, deep learning (DL) tools for drug discovery, synergy prediction, and precision medicine.

« Previous Next »
[1]
Lipinski CF, Maltarollo VG, Oliveira PR, Da Silva AB, Honorio KM. Advances and perspectives in applying deep learning for drug design and discovery. Front Robot AI 2019; 6: 108.
[http://dx.doi.org/10.3389/frobt.2019.00108]
[2]
Singh DB, Pathak RK, Rai D. From traditional herbal medicine to rational drug discovery: Strategies, challenges, and future perspectives. Rev Bras Farmacogn 2022; 32(2): 147-59.
[http://dx.doi.org/10.1007/s43450-022-00235-z]
[3]
Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov Today 2017; 22(11): 1680-5.
[http://dx.doi.org/10.1016/j.drudis.2017.08.010] [PMID: 28881183]
[4]
Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Molecules 2020; 25(6): 1375.
[http://dx.doi.org/10.3390/molecules25061375] [PMID: 32197324]
[5]
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]
[6]
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92(4): 807-12.
[http://dx.doi.org/10.1016/j.gie.2020.06.040] [PMID: 32565184]
[7]
Chan HCS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci 2019; 40(8): 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[8]
Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des 2007; 13(14): 1497-508.
[http://dx.doi.org/10.2174/138161207780765954] [PMID: 17504169]
[9]
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]
[10]
Lavecchia A. Machine-learning approaches in drug discovery: Methods and applications. Drug Discov Today 2015; 20(3): 318-31.
[http://dx.doi.org/10.1016/j.drudis.2014.10.012] [PMID: 25448759]
[11]
Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM. Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 2016; 11(3): 225-39.
[http://dx.doi.org/10.1517/17460441.2016.1146250] [PMID: 26814169]
[12]
Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform 2016; 35(1): 3-14.
[http://dx.doi.org/10.1002/minf.201501008] [PMID: 27491648]
[13]
Goodfellow I, Bengio Y, Courville A. Generative Adversarial Networks. Advances in Neural Information Processing Systems 2016; 63(11): 139-44.
[http://dx.doi.org/10.1145/3422622]
[14]
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]
[15]
Badillo S, Banfai B, Birzele F, et al. An introduction to machine learning. Clin Pharmacol Ther 2020; 107(4): 871-85.
[http://dx.doi.org/10.1002/cpt.1796] [PMID: 32128792]
[16]
Aggarwal M, Murty MN. Deep Learning. In: Machine learning in social networks. 2021; pp. 35-66.
[http://dx.doi.org/10.1007/978-981-33-4022-0_3]
[17]
Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw 2015; 61: 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003] [PMID: 25462637]
[18]
Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol 2016; 12(7): 878.
[http://dx.doi.org/10.15252/msb.20156651] [PMID: 27474269]
[19]
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5(4): 115-33.
[http://dx.doi.org/10.1007/BF02478259]
[20]
Turing AM. Computing Machinery and Intelligence. In: Parsing the turing test: philosophical and methodological issues in the quest for the thinking computer. Springer, Netherlands 2009; pp. 23-65.
[http://dx.doi.org/10.1007/978-1-4020-6710-5_3]
[21]
Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Develop 1959; 3(3): 210-29.
[http://dx.doi.org/10.1147/rd.33.0210]
[22]
Rosenblatt F. The Perceptron: A Perceiving and Recognizing Automaton,” Report 85-60-1, Cornell Aeronautical Laboratory, Buffalo, New York, 1957; 1957.
[23]
Dreyfus S. The numerical solution of variational problems. J Math Anal Appl 1962; 5(1): 30-45.
[http://dx.doi.org/10.1016/0022-247X(62)90004-5]
[24]
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]
[25]
Fukushima K. Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Netw 1988; 1(2): 119-30.
[http://dx.doi.org/10.1016/0893-6080(88)90014-7]
[26]
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323(6088): 533-6.
[http://dx.doi.org/10.1038/323533a0]
[27]
LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput 1989; 1(4): 541-51.
[http://dx.doi.org/10.1162/neco.1989.1.4.541]
[28]
Watkins CJCH, Dayan P. Q-learning. Mach Learn 1992; 8(3-4): 279-92.
[http://dx.doi.org/10.1007/BF00992698]
[29]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[30]
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997; 9(8): 1735-80.
[http://dx.doi.org/10.1162/neco.1997.9.8.1735] [PMID: 9377276]
[31]
Ilievski A, Zdraveski V, Gusev M. How CUDA Powers the Machine Learning Revolution. 2018 26th Telecommunications Forum (TELFOR). 2018;
[http://dx.doi.org/10.1109/TELFOR.2018.8611982]
[32]
Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classifcation with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012;
[33]
Le QV. Building High level Features Using Large Scale Unsupervised Learning. 2013. Avaialble from: https://arxiv.org/abs/1112.6209v5
[http://dx.doi.org/10.1109/ICASSP.2013.6639343]
[34]
Jorda M, Valero-Lara P, Pena AJ. Performance evaluation of cuDNN convolution algorithms on NVIDIA Volta GPUs. IEEE Access 2019; 7: 70461-73.
[http://dx.doi.org/10.1109/ACCESS.2019.2918851]
[35]
Taigman Y, Yang M, Ranzato M, Wolf L. DeepFace: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014; pp. 1701-8.Columbus, OH, USA.
[http://dx.doi.org/10.1109/CVPR.2014.220]
[36]
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]
[37]
Goodfellow Ian J, Jean PA, Mehdi M, et al. Generative adversarial networks. Proceedings of the 27th international conference on neural information processing systems. 2: 2672-80. Available from: https://arxiv.org/abs/1406.2661
[38]
Joo S, Kim MS, Yang J, Park J. Generative model for proposing drug candidates satisfying anticancer properties using a conditional variational autoencoder. ACS Omega 2020; 5(30): 18642-50.
[http://dx.doi.org/10.1021/acsomega.0c01149] [PMID: 32775866]
[39]
Mouchlis VD, Afantitis A, Serra A, et al. Advances in de novo drug design: From conventional to machine learning methods. Int J Mol Sci 2021; 22(4): 1676.
[http://dx.doi.org/10.3390/ijms22041676] [PMID: 33562347]
[40]
Suresh N, Chinnakonda Ashok Kumar N, Subramanian S, Srinivasa G. Memory augmented recurrent neural networks for denovo drug design. PLoS One 2022; 17(6): e0269461.
[http://dx.doi.org/10.1371/journal.pone.0269461] [PMID: 35737661]
[41]
Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G. Generative recurrent networks for de novo drug design. Mol Inform 2018; 37(1-2): 1700111.
[http://dx.doi.org/10.1002/minf.201700111] [PMID: 29095571]
[42]
Yasonik J. Multiobjective de novo drug design with recurrent neural networks and nondominated sorting. J Cheminform 2020; 12(1): 14-9.
[http://dx.doi.org/10.1186/s13321-020-00419-6] [PMID: 33430996]
[43]
Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 2018; 4(1): 120-31.
[http://dx.doi.org/10.1021/acscentsci.7b00512] [PMID: 29392184]
[44]
Ley SV, Fitzpatrick DE, Ingham RJ, Myers RM. Organic synthesis: March of the machines. Angew Chem Int Ed 2015; 54(11): 3449-64.
[http://dx.doi.org/10.1002/anie.201410744] [PMID: 25586940]
[45]
Vatansever S, Schlessinger A, Wacker D, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41(3): 1427-73.
[http://dx.doi.org/10.1002/med.21764] [PMID: 33295676]
[46]
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: 837715-12.
[http://dx.doi.org/10.3389/fphar.2022.837715] [PMID: 35359869]
[47]
Tripp A, Daxberger E, Hernández-Lobato JM. Sample-efficient optimization in the latent space of deep generative models via weighted retraining. Adv Neural Inf Process Syst 2020; 33: 11259-72.
[48]
Chenthamarakshan V, Das P, Hoffman S, et al. CogMol: Target-specific and selective drug design for COVID-19 using deep generative models. Adv Neural Inf Process Syst 2020; 33: 4320-32.
[49]
Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019; 37(9): 1038-40.
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]
[50]
Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 2018; 4(2): 268-76.
[http://dx.doi.org/10.1021/acscentsci.7b00572] [PMID: 29532027]
[51]
Kadurin A, Aliper A, Kazennov A, et al. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 2017; 8(7): 10883-90.
[http://dx.doi.org/10.18632/oncotarget.14073] [PMID: 28029644]
[52]
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]
[53]
Jacquemard C, Kellenberger E. A bright future for fragment-based drug discovery: What does it hold? Expert Opin Drug Discov 2019; 14(5): 413-6.
[http://dx.doi.org/10.1080/17460441.2019.1583643] [PMID: 30793989]
[54]
Imrie F, Bradley AR, van der Schaar M, Deane CM. Deep generative models for 3D linker design. J Chem Inf Model 2020; 60(4): 1983-95.
[http://dx.doi.org/10.1021/acs.jcim.9b01120] [PMID: 32195587]
[55]
Yang Y, Zheng S, Su S, Zhao C, Xu J, Chen H. SyntaLinker: automatic fragment linking with deep conditional transformer neural networks. Chem Sci 2020; 11(31): 8312-22.
[http://dx.doi.org/10.1039/D0SC03126G] [PMID: 34123096]
[56]
Schneider G. Generative models for artificially-intelligent molecular design. Mol Inform 2018; 37(1-2): 1880131.
[http://dx.doi.org/10.1002/minf.201880131] [PMID: 29442446]
[57]
Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform 2017; 9(1): 48.
[http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]
[58]
Erlanson DA, Fesik SW, Hubbard RE, Jahnke W, Jhoti H. Twenty years on: The impact of fragments on drug discovery. Nat Rev Drug Discov 2016; 15(9): 605-19.
[http://dx.doi.org/10.1038/nrd.2016.109] [PMID: 27417849]
[59]
Wang YW, Huang L, Jiang SW, Li K, Zou J, Yang SY. CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens. Food Chem Toxicol 2020; 135: 110921.
[http://dx.doi.org/10.1016/j.fct.2019.110921] [PMID: 31669597]
[60]
Lee M, Kim H, Joe H, Kim HG. Multi-channel PINN: Investigating scalable and transferable neural networks for drug discovery. J Cheminform 2019; 11(1): 46.
[http://dx.doi.org/10.1186/s13321-019-0368-1] [PMID: 31289963]
[61]
Radhakrishnan A, Damodaran K, Soylemezoglu AC, Uhler C, Shivashankar GV. Machine learning for nuclear mechano-morphometric biomarkers in cancer diagnosis. Sci Rep 2017; 7(1): 17946.
[http://dx.doi.org/10.1038/s41598-017-17858-1] [PMID: 29263424]
[62]
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]
[63]
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]
[64]
Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020; 180(4): 688-702.e13.
[http://dx.doi.org/10.1016/j.cell.2020.01.021] [PMID: 32084340]
[65]
Wu H, Wang C, Yin J, Lu K, Zhu L. Interpreting shared deep learning models via explicable boundary trees. arXiv 170903730, 2017.
[66]
Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB. Comparison study of computational prediction tools for drug-target binding afnities. Front Chem 2019; 7: 782.
[http://dx.doi.org/10.3389/fchem.2019.00782] [PMID: 31824921]
[67]
Hu H, Xiao A, Zhang S, et al. DeepHINT: understanding HIV-1 integration via deep learning with attention. Bioinformatics 2019; 35(10): 1660-7.
[http://dx.doi.org/10.1093/bioinformatics/bty842] [PMID: 30295703]
[68]
Wan F, Hong L, Xiao A, Jiang T, Zeng J. NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions. Bioinformatics 2019; 35(1): 104-11.
[http://dx.doi.org/10.1093/bioinformatics/bty543] [PMID: 30561548]
[69]
Öztürk H, Özgür A, Ozkirimli E. DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics 2018; 34(17): i821-9.
[http://dx.doi.org/10.1093/bioinformatics/bty593] [PMID: 30423097]
[70]
Guo Y, Li W, Wang B, Liu H, Zhou D. DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction. BMC Bioinformatics 2019; 20(1): 341.
[http://dx.doi.org/10.1186/s12859-019-2940-0] [PMID: 31208331]
[71]
Öztürk H, Ozkirimli E, Özgür A. WideDTA: Prediction of drugtarget binding afnity. arXiv: 190204166, 2019.
[72]
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): 3329-38.
[http://dx.doi.org/10.1093/bioinformatics/btz111] [PMID: 30768156]
[73]
Jiménez J, Škalič M, Martínez-Rosell G, De Fabritiis G. Deep: Protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J Chem Inf Model 2018; 58(2): 287-96.
[http://dx.doi.org/10.1021/acs.jcim.7b00650] [PMID: 29309725]
[74]
Nag S, Baidya AT, Mandal A, et al. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12(5): 110.
[http://dx.doi.org/10.1007/s13205-022-03165-8]
[75]
Ding X, Zhang B. DeepBAR: A fast and exact method for binding free energy computation. J Phys Chem Lett 2021; 12(10): 2509-15.
[http://dx.doi.org/10.1021/acs.jpclett.1c00189] [PMID: 33719449]
[76]
Wirnsberger P, Ballard AJ, Papamakarios G, et al. Targeted free energy estimation via learned mappings. J Chem Phys 2020; 153(14): 144112.
[http://dx.doi.org/10.1063/5.0018903] [PMID: 33086827]
[77]
Mayr A, Klambauer G, Unterthiner T, et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci 2018; 9(24): 5441-51.
[http://dx.doi.org/10.1039/C8SC00148K] [PMID: 30155234]
[78]
Gao KY, Fokoue A, Luo H, Iyengar A, Dey S, Zhang P. Interpretable drug target prediction using deep neural representation. IJCAI 2018; 2018: 3371-7.
[http://dx.doi.org/10.24963/ijcai.2018/468]
[79]
Feng Q, Dueva E, Cherkasov A, Ester M. Padme: A deep learning-based framework for drug-target interaction prediction. ArXivpreprint 2018.
[80]
Chushak YG, Shows HW, Gearhart JM, Pangburn HA. In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox. Toxicol Res 2018; 7(3): 423-31.
[http://dx.doi.org/10.1039/C7TX00268H] [PMID: 30090592]
[81]
Gentile F, Agrawal V, Hsing M, et al. Deep docking: A deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci 2020; 6(6): 939-49.
[http://dx.doi.org/10.1021/acscentsci.0c00229] [PMID: 32607441]
[82]
Gao K, Nguyen DD, Sresht V, Mathiowetz AM, Tu M, Wei GW. Are 2D fingerprints still valuable for drug discovery? Phys Chem Chem Phys 2020; 22(16): 8373-90.
[http://dx.doi.org/10.1039/D0CP00305K] [PMID: 32266895]
[83]
Li Y, Hu J, Wang Y, Zhou J, Zhang L, Liu Z. Deepscaffold: A comprehensive tool for scaffold-based de novo drug discovery using deep learning. J Chem Inf Model 2020; 60(1): 77-91.
[http://dx.doi.org/10.1021/acs.jcim.9b00727] [PMID: 31809029]
[84]
Nakagawa T, Miyao T, Funatsu K. Identification of bioactive scaffolds based on QSAR models. Mol Inform 2018; 37(1-2): 1700103.
[http://dx.doi.org/10.1002/minf.201700103] [PMID: 29135084]
[85]
Maragakis P, Nisonoff H, Cole B, Shaw DE. A deep-learning view of chemical space designed to facilitate drug discovery. J Chem Inf Model 2020; 60(10): 4487-96.
[http://dx.doi.org/10.1021/acs.jcim.0c00321] [PMID: 32697578]
[86]
Arora K, Bist AS. Artifcial intelligence based drug discovery techniques for covid-19 detection. Aptisi Transact Technopreneurship 2020; 2(2): 120-6.
[http://dx.doi.org/10.34306/att.v2i2.88]
[87]
Korshunova M, Ginsburg B, Tropsha A, Isayev O. OpenChem: A deep learning toolkit for computational chemistry and drug design. J Chem Inf Model 2021; 61(1): 7-13.
[http://dx.doi.org/10.1021/acs.jcim.0c00971] [PMID: 33393291]
[88]
Li J, Tong XY, Zhu LD, Zhang HY. A machine learning method for drug combination prediction. Front Genet 2020; 11: 1000.
[http://dx.doi.org/10.3389/fgene.2020.01000] [PMID: 33193585]
[89]
Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Brief Bioinform 2021; 22(6): bbab293.
[http://dx.doi.org/10.1093/bib/bbab293] [PMID: 34368832]
[90]
Zhang T, Zhang L, Payne PR, Li F. Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models. In: Translational bioinformatics for therapeutic development Humana 2021. New York, NY. pp. 223-238.
[http://dx.doi.org/10.1007/978-1-0716-0849-4_12]
[91]
Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 2020; 38(5): 672-684.e6.
[http://dx.doi.org/10.1016/j.ccell.2020.09.014] [PMID: 33096023]
[92]
Kuru HI, Tastan O, Cicek AE. MatchMaker: A deep learning framework for drug synergy prediction. IEEE/ACM Trans Comput Biol Bioinformat 2022; 19(4): 2334-44.
[http://dx.doi.org/10.1109/TCBB.2021.3086702] [PMID: 34086576]
[93]
Mei S. A machine learning framework for predicting synergistic and antagonistic drug combinatorial efficacy. J Math Chem 2022; 60(4): 752-69.
[http://dx.doi.org/10.1007/s10910-022-01331-0]
[94]
Griffin S. Diabetes precision medicine: plenty of potential, pitfalls and perils but not yet ready for prime time. Diabetologia 2022; 65(11): 1913-21.
[http://dx.doi.org/10.1007/s00125-022-05782-7] [PMID: 35999379]
[95]
Nguyen TM, Kim N, Kim DH, et al. Deep learning for human disease detection, subtype classification, and treatment response prediction using epigenomic data. Biomedicines 2021; 9(11): 1733.
[http://dx.doi.org/10.3390/biomedicines9111733] [PMID: 34829962]
[96]
Zhao J, Feng Q, Wei WQ. Integration of omics and phenotypic data for precision medicine. In: Systems Medicine. Humana New York, NY 2022; pp. 19-35.
[http://dx.doi.org/10.1007/978-1-0716-2265-0_2]
[97]
Chen R, Yang L, Goodison S, Sun Y. Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics 2020; 36(5): 1476-83.
[http://dx.doi.org/10.1093/bioinformatics/btz769] [PMID: 31603461]
[98]
Teng H, Cao MD, Hall MB, Duarte T, Wang S, Coin LJM. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. Gigascience 2018; 7(5): giy037.
[http://dx.doi.org/10.1093/gigascience/giy037] [PMID: 29648610]
[99]
Naert T, Çiçek Ö, Ogar P, et al. Deep learning is widely applicable to phenotyping embryonic development and disease. Development 2021; 148(21): dev199664.
[http://dx.doi.org/10.1242/dev.199664] [PMID: 34739029]
[100]
Ramachandran A, Lumetta SS, Klee EW, Chen D. HELLO: Improved neural network architectures and methodologies for small variant calling. BMC Bioinformatics 2021; 22(1): 404.
[http://dx.doi.org/10.1186/s12859-021-04311-4] [PMID: 34391391]
[101]
Ainscough BJ, Barnell EK, Ronning P, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet 2018; 50(12): 1735-43.
[http://dx.doi.org/10.1038/s41588-018-0257-y] [PMID: 30397337]
[102]
Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 2015; 12(10): 931-4.
[http://dx.doi.org/10.1038/nmeth.3547] [PMID: 26301843]
[103]
Liu Q, Cheng X, Liu G, Li B, Liu X. Deep learning improves the ability of sgRNA off-target propensity prediction. BMC Bioinformatics 2020; 21(1): 51.
[http://dx.doi.org/10.1186/s12859-020-3395-z] [PMID: 32041517]
[104]
Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm 2016; 13(7): 2524-30.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00248] [PMID: 27200455]
[105]
Tran NH, Qiao R, Xin L, Chen X, Shan B, Li M. Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. Nat Mach Intell 2020; 2(12): 764-71.
[http://dx.doi.org/10.1038/s42256-020-00260-4]
[106]
Kalinin AA, Higgins GA, Reamaroon N, et al. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 2018; 19(7): 629-50.
[http://dx.doi.org/10.2217/pgs-2018-0008] [PMID: 29697304]
[107]
Shirazi AZ, Fornaciari E, Gomez GA. deep Learning in Precision Medicine. In: Artificial Intelligence in Precision Health. Academic Press: Cambridge 2020; pp. 61-90.
[http://dx.doi.org/10.1016/B978-0-12-817133-2.00003-3]
[108]
Bao XR, Zhu YH, Yu DJ. DeepTF: Accurate prediction of transcription factor binding sites by combining multi-scale convolution and long short-term memory neural network. In: Intelligent science and big data engineering Big data and machine learning. Springer, Cham 2019; 11936: pp. 126-38.
[109]
Chen C, Hou J, Shi X, Yang H, Birchler JA, Cheng J. DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks. BMC Bioinformat 2021; 22(1): 38.
[http://dx.doi.org/10.1186/s12859-020-03952-1] [PMID: 33522898]
[110]
Lei Y, Li S, Liu Z, et al. A deep-learning framework for multi-level peptide–protein interaction prediction. Nat Commun 2021; 12(1): 5465.
[http://dx.doi.org/10.1038/s41467-021-25772-4] [PMID: 34526500]
[111]
Wang P, Zhang G, Yu ZG, Huang G. A deep learning and XGBoost-based method for predicting protein-protein interaction sites. Front Genet 2021; 12: 752732.
[http://dx.doi.org/10.3389/fgene.2021.752732] [PMID: 34764983]
[112]
Avsec Ž, Agarwal V, Visentin D, et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 2021; 18(10): 1196-203.
[http://dx.doi.org/10.1038/s41592-021-01252-x] [PMID: 34608324]
[113]
White paper - zeptonet, a novel AI technology for virtual screening of small molecules. Available from: https://www.kantify.com/insights/a-transformative-ai-approach-to-small-molecule-screening
[114]
Chiu YC, Chen HIH, Zhang T, et al. Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Med Genomics 2019; 12(1): 119.
[http://dx.doi.org/10.1186/s12920-019-0569-5] [PMID: 31405368]
[115]
Zuo Z, Wang P, Chen X, Tian L, Ge H, Qian D. SWnet: A deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures. BMC Bioinformat 2021; 22(1): 434.
[http://dx.doi.org/10.1186/s12859-021-04352-9] [PMID: 34507532]
[116]
Eickhoff K. Navigating ownership in the context of the security sector reform (SSR) in mali: A comparison of external actors’ approaches. J Interv Statebuilding 2021; 15(3): 386-405.
[http://dx.doi.org/10.1080/17502977.2020.1833582]
[117]
Zhang Y, Ye T, Xi H, Juhas M, Li J. Deep learning driven drug discovery: tackling severe acute respiratory syndrome coronavirus 2. Front Microbiol 2021; 12: 739684.
[http://dx.doi.org/10.3389/fmicb.2021.739684] [PMID: 34777286]
[118]
Golkov V, Skwark MJ, Mirchev A, et al. 3D deep learning for biological function prediction from physical fields In 2020 International Conference on 3D Vision (3DV). IEEE 2020; pp. 928-37.
[119]
Miyao T, Kaneko H, Funatsu K. Inverse QSPR/QSAR analysis for chemical structure generation (from y to x). J Chem Inf Model 2016; 56(2): 286-99.
[http://dx.doi.org/10.1021/acs.jcim.5b00628] [PMID: 26818135]
[120]
Xu Y, Lin K, Wang S, et al. Deep learning for molecular generation. Future Med Chem 2019; 11(6): 567-97.
[http://dx.doi.org/10.4155/fmc-2018-0358] [PMID: 30698019]
[121]
Pogány P, Arad N, Genway S, Pickett SD. De novo molecule design by translating from reduced graphs to SMILES. J Chem Inf Model 2019; 59(3): 1136-46.
[http://dx.doi.org/10.1021/acs.jcim.8b00626] [PMID: 30525594]
[122]
Wang M, Wang Z, Sun H, et al. Deep learning approaches for de novo drug design: An overview. Curr Opin Struct Biol 2022; 72: 135-44.
[http://dx.doi.org/10.1016/j.sbi.2021.10.001] [PMID: 34823138]
[123]
Turzo SMBA, Hantz ER, Lindert S. Applications of machine learning in computer-aided drug discovery. QRB Discovery 2022; 3: e14.
[http://dx.doi.org/10.1017/qrd.2022.12]
[124]
Lee I, Keum J, Nam H. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLOS Comput Biol 2019; 15(6): e1007129.
[http://dx.doi.org/10.1371/journal.pcbi.1007129] [PMID: 31199797]
[125]
Altae-Tran H, Ramsundar B, Pappu AS, Pande V. Low data drug discovery with one-shot learning. ACS Cent Sci 2017; 3(4): 283-93.
[http://dx.doi.org/10.1021/acscentsci.6b00367] [PMID: 28470045]
[126]
Tian K, Shao M, Wang Y, Guan J, Zhou S. Boosting compound-protein interaction prediction by deep learning. Methods 2016; 110: 64-72.
[http://dx.doi.org/10.1016/j.ymeth.2016.06.024] [PMID: 27378654]
[127]
Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm 2016; 13(5): 1445-54.https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.5b00982
[http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982] [PMID: 27007977]
[128]
Newby D, Freitas AA, Ghafourian T. Comparing multilabel classification methods for provisional biopharmaceutics class prediction. Mol Pharm 2015; 12(1): 87-102.
[http://dx.doi.org/10.1021/mp500457t] [PMID: 25397721]
[129]
Jenkins J, Schirle M. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov Today 2016; 21(1): 82-9.
[130]
Rifaioglu AS, Nalbat E, Atalay V, Martin MJ, Cetin-Atalay R, Doğan T. DEEPScreen: High performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci (Camb) 2020; 11(9): 2531-57.https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc03414e
[http://dx.doi.org/10.1039/C9SC03414E] [PMID: 33209251]
[131]
Dana D, Gadhiya SV, St Surin LG, et al. Deep learning in drug discovery and medicine; scratching the surface. Molecules 2018; 23(9): 2384.
[http://dx.doi.org/10.3390/molecules23092384] [PMID: 30231499]
[132]
Iskar M, Zeller G, Blattmann P, et al. Characterization of drug-induced transcriptional modules: Towards drug repositioning and functional understanding. Mol Syst Biol 2013; 9(1): 662.
[http://dx.doi.org/10.1038/msb.2013.20] [PMID: 23632384]
[133]
Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4(2): 90-8.
[http://dx.doi.org/10.1038/nchem.1243] [PMID: 22270643]

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