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 Artificial Intelligence Approaches and Rational Approaches in Drug Discovery

Author(s): Anjana Vidya Srivathsa, Nandini Markuli Sadashivappa, Apeksha Krishnamurthy Hegde, Srimathi Radha, Agasa Ramu Mahesh, Damodar Nayak Ammunje, Debanjan Sen, Panneerselvam Theivendren, Saravanan Govindaraj, Selvaraj Kunjiappan* and Parasuraman Pavadai*

Volume 29, Issue 15, 2023

Published on: 12 May, 2023

Page: [1180 - 1192] Pages: 13

DOI: 10.2174/1381612829666230428110542

Price: $65

Abstract

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain’s thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.

Keywords: Artificial intelligence, neural network, drug design, virtual screening, toxicity, recursive neural network.

« Previous Next »
[1]
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162(6): 1239-49.
[http://dx.doi.org/10.1111/j.1476-5381.2010.01127.x] [PMID: 21091654]
[2]
Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009-2018. JAMA 2020; 323(9): 844-53.
[http://dx.doi.org/10.1001/jama.2020.1166] [PMID: 32125404]
[3]
Tai MT. The impact of artificial intelligence on human society and bioethics. Tzu-Chi Med J 2020; 32(4): 339-43.
[http://dx.doi.org/10.4103/tcmj.tcmj_71_20] [PMID: 33163378]
[4]
Gentile F, Yaacoub JC, Gleave J, et al. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 2022; 17(3): 672-97.
[http://dx.doi.org/10.1038/s41596-021-00659-2] [PMID: 35121854]
[5]
Vijayan RSK, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today 2022; 27(4): 967-84.
[http://dx.doi.org/10.1016/j.drudis.2021.11.023] [PMID: 34838731]
[6]
Polishchuk PG, Madzhidov TI, Varnek A. Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 2013; 27(8): 675-9.
[http://dx.doi.org/10.1007/s10822-013-9672-4] [PMID: 23963658]
[7]
Liu Z, Roberts RA, Lal-Nag M, Chen X, Huang R, Tong W. AI-based language models powering drug discovery and development. Drug Discov Today 2021; 26(11): 2593-607.
[http://dx.doi.org/10.1016/j.drudis.2021.06.009] [PMID: 34216835]
[8]
Giorgi JM, Bader GD. Towards reliable named entity recognition in the biomedical domain. Bioinformatics 2020; 36(1): 280-6.
[http://dx.doi.org/10.1093/bioinformatics/btz504] [PMID: 31218364]
[9]
Wang X, Zhang Y, Ren X, et al. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 2019; 35(10): 1745-52.
[http://dx.doi.org/10.1093/bioinformatics/bty869] [PMID: 30307536]
[10]
Fabian B, Edlich T, Gaspar H, et al. Molecular representation learning with language models and domain-relevant auxiliary tasks. ArXiv 2020. ArXiv:2011.13230
[11]
Nag S, Baidya ATK, Mandal A, et al. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12: 1-21.
[12]
Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J 2012; 14(1): 133-41.
[http://dx.doi.org/10.1208/s12248-012-9322-0] [PMID: 22281989]
[13]
Lionta E, Spyrou G, Vassilatis D, Cournia Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr Top Med Chem 2014; 14(16): 1923-38.
[http://dx.doi.org/10.2174/1568026614666140929124445] [PMID: 25262799]
[14]
Hamza A, Wei NN, Zhan CG. Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model 2012; 52(4): 963-74.
[http://dx.doi.org/10.1021/ci200617d] [PMID: 22486340]
[15]
Anderson AC. The process of structure-based drug design. Chem Biol 2003; 10(9): 787-97.
[http://dx.doi.org/10.1016/j.chembiol.2003.09.002] [PMID: 14522049]
[16]
Krüger DM, Evers A. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 2010; 5(1): 148-58.
[http://dx.doi.org/10.1002/cmdc.200900314] [PMID: 19908272]
[17]
Batool M, Ahmad B, Choi S. A structure-based drug discovery paradigm. Int J Mol Sci 2019; 20(11): 2783.
[http://dx.doi.org/10.3390/ijms20112783] [PMID: 31174387]
[18]
Wlodawer A, Vondrasek J. Inhibitors of HIV-1 protease: A major success of structure-assisted drug design. Annu Rev Biophys Biomol Struct 1998; 27(1): 249-84.
[http://dx.doi.org/10.1146/annurev.biophys.27.1.249] [PMID: 9646869]
[19]
Clark DE. What has computer-aided molecular design ever done for drug discovery? Expert Opin Drug Discov 2006; 1(2): 103-10.
[http://dx.doi.org/10.1517/17460441.1.2.103] [PMID: 23495794]
[20]
Rutenber EE, Stroud RM. Binding of the anticancer drug ZD1694 to E. coli thymidylate synthase: Assessing specificity and affinity. Structure 1996; 4(11): 1317-24.
[http://dx.doi.org/10.1016/S0969-2126(96)00139-6] [PMID: 8939755]
[21]
Lyne PD. Structure-based virtual screening: An overview. Drug Discov Today 2002; 7(20): 1047-55.
[http://dx.doi.org/10.1016/S1359-6446(02)02483-2] [PMID: 12546894]
[22]
Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform 2009; 10(5): 579-91.
[http://dx.doi.org/10.1093/bib/bbp023] [PMID: 19433475]
[23]
Krieger E, Joo K, Lee J, et al. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins 2009; 77 (Suppl. 9): 114-22.
[http://dx.doi.org/10.1002/prot.22570] [PMID: 19768677]
[24]
Laurie ATR, Jackson RM. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005; 21(9): 1908-16.
[http://dx.doi.org/10.1093/bioinformatics/bti315] [PMID: 15701681]
[25]
Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: A powerful approach for structure-based drug discovery. Curr Computeraided Drug Des 2011; 7(2): 146-57.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[26]
Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci 2010; 11(8): 3016-34.
[http://dx.doi.org/10.3390/ijms11083016] [PMID: 21152288]
[27]
López-Vallejo F, Caulfield T, Martínez-Mayorga K, et al. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb Chem High Throughput Screen 2011; 14(6): 475-87.
[http://dx.doi.org/10.2174/138620711795767866] [PMID: 21521151]
[28]
Kapetanovic IM. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem Biol Interact 2008; 171(2): 165-76.
[http://dx.doi.org/10.1016/j.cbi.2006.12.006] [PMID: 17229415]
[29]
Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip Rev Comput Mol Sci 2015; 5(6): 405-24.
[http://dx.doi.org/10.1002/wcms.1225] [PMID: 27110292]
[30]
Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR. Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go. Br J Pharmacol 2008; 153 (Suppl. 1): S7-S26.
[http://dx.doi.org/10.1038/sj.bjp.0707515] [PMID: 18037925]
[31]
Guedes IA, Pereira FSS, Dardenne LE. Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Front Pharmacol 2018; 9: 1089.
[http://dx.doi.org/10.3389/fphar.2018.01089] [PMID: 30319422]
[32]
Li H, Peng J, Leung Y, et al. The impact of protein structure and sequence similarity on the accuracy of machine-learning scoring functions for binding affinity prediction. Biomolecules 2018; 8(1): 12.
[http://dx.doi.org/10.3390/biom8010012] [PMID: 29538331]
[33]
Hecht D, Fogel G. Computational intelligence methods for docking scores. Curr Computeraided Drug Des 2009; 5(1): 56-68.
[http://dx.doi.org/10.2174/157340909787580863]
[34]
Feher M. Consensus scoring for protein-ligand interactions. Drug Discov Today 2006; 11(9-10): 421-8.
[http://dx.doi.org/10.1016/j.drudis.2006.03.009] [PMID: 16635804]
[35]
Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: Current status and future challenges. Proteins 2006; 65(1): 15-26.
[http://dx.doi.org/10.1002/prot.21082] [PMID: 16862531]
[36]
Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery 2005; 4: 649-63.
[http://dx.doi.org/10.1038/nrd1799]
[37]
Schneider P, Schneider G. De novo design at the edge of chaos. J Med Chem 2016; 59(9): 4077-86.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01849] [PMID: 26881908]
[38]
Danziger DJ, Dean PM. Automated site-directed drug design: A general algorithm for knowledge acquisition about hydrogen-bonding regions at protein surfaces. Proc R Soc Lond B Biol Sci 1989; 236(1283): 101-13.
[http://dx.doi.org/10.1098/rspb.1989.0015] [PMID: 2565575]
[39]
Zhu J, Fan H, Liu H, Shi Y. Structure-based ligand design for flexible proteins: Application of new F-DycoBlock. J Comput Aided Mol Des 2001; 11: 979-96.
[http://dx.doi.org/10.1023/A:1014817911249]
[40]
Wise A, Gearing K, Rees S. Target validation of G-protein coupled receptors. Drug Discov Today 2002; 7(4): 235-46.
[http://dx.doi.org/10.1016/S1359-6446(01)02131-6] [PMID: 11839521]
[41]
Waszkowycz B, Clark DE, Frenkel D, et al. PRO_LIGAND: An approach to de novo molecular design. 2. Design of novel molecules from molecular field analysis (MFA) models and pharmacophores. J Med Chem 1994; 37(23): 3994-4002.
[http://dx.doi.org/10.1021/jm00049a019] [PMID: 7966160]
[42]
Afantitis A, Melagraki G, Koutentis PA, Sarimveis H, Kollias G. Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks. Eur J Med Chem 2011; 46(2): 497-508.
[http://dx.doi.org/10.1016/j.ejmech.2010.11.029] [PMID: 21167625]
[43]
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]
[44]
Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu Rev Pharmacol Toxicol 2020; 60(1): 573-89.
[http://dx.doi.org/10.1146/annurev-pharmtox-010919-023324] [PMID: 31518513]
[45]
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]
[46]
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23(6): 1241-50.
[http://dx.doi.org/10.1016/j.drudis.2018.01.039] [PMID: 29366762]
[47]
Han M, Zhao J, Zhang X, Shen J, Li Y. The reinforcement learning method for occupant behavior in building control: A review. Energy and Built Environment 2021; 2(2): 137-48.
[http://dx.doi.org/10.1016/j.enbenv.2020.08.005]
[48]
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]
[49]
Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. ArXiv 2015. ArXiv: 1506.00019
[50]
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]
[51]
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 2020; 11(9): 2531-57.
[http://dx.doi.org/10.1039/C9SC03414E] [PMID: 33209251]
[52]
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-44.
[http://dx.doi.org/10.1038/nature14539]
[53]
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 2020.
[54]
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]
[55]
Hartenfeller M, Proschak E, Schüller A, Schneider G. Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization. Chem Biol Drug Des 2008; 72(1): 16-26.
[http://dx.doi.org/10.1111/j.1747-0285.2008.00672.x] [PMID: 18564216]
[56]
Cherkasov A, Muratov EN, Fourches D, et al. QSAR modeling: Where have you been? Where are you going to? J Med Chem 2014; 57(12): 4977-5010.
[http://dx.doi.org/10.1021/jm4004285] [PMID: 24351051]
[57]
Lenselink EB, ten Dijke N, Bongers B, et al. Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J Cheminform 2017; 9(1): 45.
[http://dx.doi.org/10.1186/s13321-017-0232-0] [PMID: 29086168]
[58]
Liu X, Ye K, van Vlijmen HWT, IJzerman AP, van Westen GJP. An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: A case for the adenosine A2A receptor. J Cheminform 2019; 11(1): 35.
[http://dx.doi.org/10.1186/s13321-019-0355-6] [PMID: 31127405]
[59]
Merk D, Friedrich L, Grisoni F, Schneider G. De Novo design of bioactive small molecules by artificial intelligence. Mol Inform 2018; 37(1-2): 1700153.
[http://dx.doi.org/10.1002/minf.201700153] [PMID: 29319225]
[60]
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]
[61]
Ståhl N, Falkman G, Karlsson A, Mathiason G, Boström J. Deep reinforcement learning for multiparameter optimization in de novo drug design. J Chem Inf Model 2019; 59(7): 3166-76.
[http://dx.doi.org/10.1021/acs.jcim.9b00325] [PMID: 31273995]
[62]
Khemchandani Y, O’Hagan S, Samanta S, et al. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: A graph convolution and reinforcement learning approach. J Cheminform 2020; 12(1): 53.
[http://dx.doi.org/10.1186/s13321-020-00454-3] [PMID: 33431037]
[63]
Putin E, Asadulaev A, Vanhaelen Q, et al. Adversarial threshold neural computer for molecular de novo design. Mol Pharm 2018; 15(10): 4386-97.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b01137] [PMID: 29569445]
[64]
Zang Q, Mansouri K, Williams AJ, et al. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J Chem Inf Model 2017; 57(1): 36-49.
[http://dx.doi.org/10.1021/acs.jcim.6b00625] [PMID: 28006899]
[65]
Hessler G, Baringhaus KH. Artificial intelligence in drug design. Molecules 2018; 23(10): 2520.
[http://dx.doi.org/10.3390/molecules23102520] [PMID: 30279331]
[66]
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]
[67]
Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 2013; 53(7): 1563-75.
[http://dx.doi.org/10.1021/ci400187y] [PMID: 23795551]
[68]
Kumar R, Sharma A, Siddiqui MH, Tiwari RK. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr Drug Discov Technol 2017; 14(4): 244-54.
[http://dx.doi.org/10.2174/1570163814666170404160911] [PMID: 28382857]
[69]
Feng Q, Dueva E, Cherkasov A, Ester M. PADME: A deep learning-based framework for drug-target interaction prediction. ArXiv 2018. ArXiv:1807.09741
[70]
Muratov EN, Bajorath J, Sheridan RP, et al. QSAR without borders. Chem Soc Rev 2020; 49(11): 3525-64.
[http://dx.doi.org/10.1039/D0CS00098A] [PMID: 32356548]
[71]
Wu Y, Wang G. Machine learning based toxicity prediction: From Chemical structural description to transcriptome analysis. Int J Mol Sci 2018; 19(8): 2358.
[http://dx.doi.org/10.3390/ijms19082358] [PMID: 30103448]
[72]
Karpov P, Godin G, Tetko IV. Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J Cheminform 2020; 12(1): 17.
[http://dx.doi.org/10.1186/s13321-020-00423-w] [PMID: 33431004]
[73]
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]
[74]
Lounkine E, Keiser MJ, Whitebread S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 2012; 486: 361-7.
[http://dx.doi.org/10.1038/nature11159]
[75]
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. EToxPred: A machine learning-based approach to estimate the toxicity of drug candidates 11 Medical and Health Sciences 1115 Pharmacology and Pharmaceutical Sciences 03 Chemical Sciences 0305 Organic Chemistry 03 Chemical Sciences 0304 Medicinal and Biomolecular Chemistry. BMC Pharmacol Toxicol 2019; 20: 1-15.
[http://dx.doi.org/10.1186/S40360-018-0282-6/FIGURES/10]
[76]
Jeong J, Choi J. Artificial intelligence-based toxicity prediction of environmental chemicals: Future directions for chemical management applications. Environ Sci Technol 2022; 56(12): 7532-43.
[http://dx.doi.org/10.1021/acs.est.1c07413] [PMID: 35666838]
[77]
Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: Toxicity prediction using deep learning. Front Environ Sci 2016; 3: 80.
[http://dx.doi.org/10.3389/fenvs.2015.00080]
[78]
Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance 2018; 1(6): e201800098.
[http://dx.doi.org/10.26508/lsa.201800098] [PMID: 30515477]
[79]
Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 2015; 55(2): 263-74.
[http://dx.doi.org/10.1021/ci500747n] [PMID: 25635324]
[80]
Varnek A, Gaudin C, Marcou G, Baskin I, Pandey AK, Tetko IV. Inductive transfer of knowledge: Application of multi-task learning and feature net approaches to model tissue-air partition coefficients. J Chem Inf Model 2009; 49(1): 133-44.
[http://dx.doi.org/10.1021/ci8002914] [PMID: 19125628]
[81]
Coley CW, Barzilay R, Green WH, Jaakkola TS, Jensen KF. Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model 2017; 57(8): 1757-72.
[http://dx.doi.org/10.1021/acs.jcim.6b00601] [PMID: 28696688]
[82]
Ankley GT, Bennett RS, Erickson RJ, et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 2010; 29(3): 730-41.
[http://dx.doi.org/10.1002/etc.34] [PMID: 20821501]
[83]
Pittman ME, Edwards SW, Ives C, Mortensen HM. AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks. Toxicol Appl Pharmacol 2018; 343: 71-83.
[http://dx.doi.org/10.1016/j.taap.2018.02.006] [PMID: 29454060]
[84]
Ashburn TT, Thor KB. Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004; 3: 673-83.
[http://dx.doi.org/10.1038/nrd1468]
[85]
Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 2012; 11(3): 191-200.
[http://dx.doi.org/10.1038/nrd3681] [PMID: 22378269]
[86]
Breckenridge A, Jacob R. Overcoming the legal and regulatory barriers to drug repurposing. Nat Rev Drug Discov 2019; 18(1): 1-2.
[http://dx.doi.org/10.1038/nrd.2018.92] [PMID: 29880920]
[87]
Nishimura Y, Hara H. Editorial: Drug repositioning: Current advances and future perspectives. Front Pharmacol 2018; 9: 1068.
[http://dx.doi.org/10.3389/fphar.2018.01068] [PMID: 30294274]
[88]
Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: Progress, challenges and recommendations. Nat Rev Drug Discov 2018; 18: 41-58.
[http://dx.doi.org/10.1038/nrd.2018.168]
[89]
Shoichet BK, McGovern SL, Wei B, Irwin JJ. Lead discovery using molecular docking. Curr Opin Chem Biol 2002; 6(4): 439-46.
[http://dx.doi.org/10.1016/S1367-5931(02)00339-3] [PMID: 12133718]
[90]
Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature 2009; 462(7270): 175-81.
[http://dx.doi.org/10.1038/nature08506] [PMID: 19881490]
[91]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49.
[http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816]
[92]
Sanseau P, Agarwal P, Barnes MR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol 2012; 30: 317-20.
[http://dx.doi.org/10.1038/nbt.2151]
[93]
Yu H, Li C, Wang X, et al. Techniques and strategies for potential protein target discovery and active pharmaceutical molecule screening in a pandemic. J Proteome Res 2020; 19(11): 4242-58.
[http://dx.doi.org/10.1021/acs.jproteome.0c00372] [PMID: 32957788]
[94]
Lage O, Ramos M, Calisto R, Almeida E, Vasconcelos V, Vicente F. Current screening methodologies in drug discovery for selected human diseases. Mar Drugs 2018; 16(8): 279.
[http://dx.doi.org/10.3390/md16080279] [PMID: 30110923]
[95]
Singh TU, Parida S, Lingaraju MC, Kesavan M, Kumar D, Singh RK. Drug repurposing approach to fight COVID-19. Pharmacol Rep 2020; 72(6): 1479-508.
[http://dx.doi.org/10.1007/s43440-020-00155-6] [PMID: 32889701]
[96]
Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382(8): 727-33.
[http://dx.doi.org/10.1056/NEJMoa2001017] [PMID: 31978945]
[97]
Zhang H, Saravanan KM, Yang Y, et al. Deep learning based drug screening for novel coronavirus 2019-nCov. Interdiscip Sci 2020; 12(3): 368-76.
[http://dx.doi.org/10.1007/s12539-020-00376-6] [PMID: 32488835]
[98]
Nilamyani AN, Auliah FN, Moni MA, Shoombuatong W, Hasan MM, Kurata H. PredNTS: Improved and robust prediction of nitrotyrosine sites by integrating multiple sequence features. Int J Mol Sci 2021; 22: 2704.
[http://dx.doi.org/10.3390/ijms22052704]
[99]
Belyaeva A, Cammarata L, Radhakrishnan A, et al. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nat Commun 2021; 12: 1-13.
[http://dx.doi.org/10.1038/s41467-021-21056-z]
[100]
Mei S, Li F, Leier A, et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief Bioinform 2020; 21(4): 1119-35.
[http://dx.doi.org/10.1093/bib/bbz051] [PMID: 31204427]
[101]
Su X, Chen N, Sun H, et al. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro-oncol 2019; 22(3): noz184.
[http://dx.doi.org/10.1093/neuonc/noz184] [PMID: 31563963]
[102]
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]
[103]
Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov 2021; 11(4): 900-15.
[http://dx.doi.org/10.1158/2159-8290.CD-21-0090] [PMID: 33811123]
[104]
Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence 2021; 3: 199-217.
[http://dx.doi.org/10.1038/s42256-021-00307-0]
[105]
Muratov EN, Amaro R, Andrade CH, et al. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50(16): 9121-51.
[http://dx.doi.org/10.1039/D0CS01065K] [PMID: 34212944]
[106]
Julkunen H, Cichonska A, Gautam P, et al. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nat Commun 2020; 11: 1-11.
[http://dx.doi.org/10.1038/s41467-020-19950-z]
[107]
Attwood MM, Fabbro D, Sokolov AV, Knapp S, Schiöth HB. Trends in kinase drug discovery: Targets, indications and inhibitor design. Nat Rev Drug Discov 2021; 20(11): 839-61.
[http://dx.doi.org/10.1038/s41573-021-00252-y] [PMID: 34354255]
[108]
Aittokallio T. What are the current challenges for machine learning in drug discovery and repurposing? Expert Opin Drug Discov 17(4): 1-3.
[http://dx.doi.org/10.1080/17460441.2022.2050694]
[109]
Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, de Preter K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nature Communications 2020; 11: 1-14.
[http://dx.doi.org/10.1038/s41467-020-19015-1]
[110]
Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nature Machine Intelligence 2020; 2: 573-84.
[http://dx.doi.org/10.1038/s42256-020-00236-4]
[111]
Thakur A, Mishra AP, Panda B, Rodríguez DCS, Gaurav I, Majhi B. Application of artificial intelligence in pharmaceutical and biomedical studies. Curr Pharm Des 2020; 26(29): 3569-78.
[http://dx.doi.org/10.2174/1381612826666200515131245] [PMID: 32410553]

Rights & Permissions Print Cite
Article Metrics
68
2
World Health Day
© 2024 Bentham Science Publishers | Privacy Policy