Skip to content
2000
Volume 6, Issue 3
  • ISSN: 2666-7967
  • E-ISSN: 2666-7975

Abstract

Background

Adaptation and application of Artificial Intelligence (AI) technology for the development of drugs against the deadly and continuously mutating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been extremely beneficial, cost-effective, and time saving for the scientific community. A systematic review is necessary for complete picturization of the overall AI assistance in developing drugs and vaccines against SARS-CoV-2.

Materials and Methods

A systematic analysis and review of the research literature available on the application of AI in the development of drugs and vaccines against SARS-CoV-2 from various online platforms like Web of Science, PubMed Central, Science Direct, ResearchGate, Scopus, Google Scholar, Medline, Embase ., has been performed, and relevant full papers have been selected on certain selection criteria and have been used for this review.

Results

Utilization of AI tools has enabled the selection, modification, evaluation, and prediction of the effectiveness of drug formulations against coronavirus disease (COVID-19) in a very rapid and efficient manner. Vaccine development against the deadly SARS-CoV-2 has also been aided and benefited immensely by using AI tools and technique.

Discussion

Thousands of studies regarding the development of effective drugs and vaccines against the constantly evolving, mutating, and prevailing SARS-CoV-2 have been conducted, and several thousands are still being conducted around the world.

Conclusion

AI is a powerful tool, and its application has been highly beneficial in developing effective drugs and vaccines against the deadly SARS-CoV-2 in a cost-effective and time-effective frame. This systematic review briefs the findings and achievements till the date of writing this article in the field of AI-assisted drug and vaccine development against COVID-19.

Loading

Article metrics loading...

/content/journals/covid/10.2174/0126667975309811240530114325
2024-06-07
2025-09-27
Loading full text...

Full text loading...

References

  1. KimS. COVID-19 drug development.J. Microbiol. Biotechnol.20223211510.4014/jmb.2110.10029 34866128
    [Google Scholar]
  2. WuC. ChengJ. ZouJ. DuanL. CampbellJ.E. Health-related quality of life of hospitalized COVID-19 survivors: An initial exploration in Nanning city, China.Soc. Sci. Med.202127411374810.1016/j.socscimed.2021.113748 33648821
    [Google Scholar]
  3. von DelftA. HallM.D. KwongA.D. Accelerating antiviral drug discovery: Lessons from COVID-19.Nat. Rev. Drug Discov.202322758560310.1038/s41573‑023‑00692‑8 37173515
    [Google Scholar]
  4. MonteleoneS. KelliciT.F. SoutheyM. BodkinM.J. HeifetzA. Fighting COVID-19 with artificial intelligence.Methods Mol. Biol.2022239010311210.1007/978‑1‑0716‑1787‑8_3 34731465
    [Google Scholar]
  5. ZhouY. WangF. TangJ. NussinovR. ChengF. Artificial intelligence in COVID-19 drug repurposing.Lancet Digit. Health2020212e667e67610.1016/S2589‑7500(20)30192‑8 32984792
    [Google Scholar]
  6. BarnawiA. ChhikaraP. TekchandaniR. KumarN. AlzahraniB. Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging.Future Gener. Comput. Syst.202112411913210.1016/j.future.2021.05.019 34075265
    [Google Scholar]
  7. ZahradníkJ. MarcianoS. ShemeshM. SARS-CoV-2 variant prediction and antiviral drug design are enabled by RBD in vitro evolution.Nat. Microbiol.2021691188119810.1038/s41564‑021‑00954‑4 34400835
    [Google Scholar]
  8. COVID-19 epidemiological.Available from: https://www.who.int/publications/m/item/covid-19-epidemiological-update---22-december-2023 22-december-2023.2023
  9. SharmaA. VirmaniT. PathakV. Artificial intelligence-based data-driven strategy to accelerate research, development, and clinical trials of covid vaccine.BioMed Res. Int.2022202211610.1155/2022/7205241 35845955
    [Google Scholar]
  10. BaliA BaliN Role of artificial intelligence in fast-track drug discovery and vaccine development for COVID-19.Novel AI and Data Science Advancements for Sustainability in the Era of COVID-1920222012910.1016/B978‑0‑323‑90054‑6.00006‑4
    [Google Scholar]
  11. YanL. ZhangH.T. GoncalvesJ. An interpretable mortality prediction model for COVID-19 patients.Nat. Mach. Intell.20202528328810.1038/s42256‑020‑0180‑7
    [Google Scholar]
  12. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.010 33099022
    [Google Scholar]
  13. OzdemirE.S. RanganathanS.V. NussinovR. How has artificial intelligence impacted COVID-19 drug repurposing and what lessons have we learned?Expert Opin. Drug Discov.202217101061106510.1080/17460441.2022.2128333 36154343
    [Google Scholar]
  14. SadeghiporN. AghdamB.H. Investigating the effect of appropriate personal protective equipment on the stress level of care workers in the COVID-19 epidemic.Health Sci. J.2021734
    [Google Scholar]
  15. KieslichC.A. AlimirzaeiF. SongH. DoM. HallP. Data-driven prediction of antiviral peptides based on periodicities of amino acid properties.Computer-Aided Chem. Eng.2021502019202410.1016/B978‑0‑323‑88506‑5.50312‑0
    [Google Scholar]
  16. ShahparvariS. HassanizadehB. MohammadiA. A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.Transp. Res., Part E Logist. Trans. Rev.202215910259810.1016/j.tre.2021.102598 35185357
    [Google Scholar]
  17. FarsaeivahidN. GrenierC. NazarianS. WangM.L. A rapid label-free disposable electrochemical salivary point-of-care sensor for sars-cov-2 detection and quantification.Sensors202223143310.3390/s23010433 36617031
    [Google Scholar]
  18. FouladiS. EbadiM.J. SafaeiA.A. BajuriM.Y. AhmadianA. Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.Comput. Commun.202117623424810.1016/j.comcom.2021.06.011 34149118
    [Google Scholar]
  19. VidhyaK.S. SultanaA. MN.K. RangareddyH. Artificial intelligence’s impact on drug discovery and development from bench to bedside.Cureus20231510e4748610.7759/cureus.47486 37881323
    [Google Scholar]
  20. PageM.J. McKenzieJ.E. BossuytP.M. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews.BMJ202137271n7110.1136/bmj.n71 33782057
    [Google Scholar]
  21. BenekeF. MackenrodtM.O. Artificial intelligence and collusion.IIC Int. Rev. Ind. Prop. Copyr. Law201950110913410.1007/s40319‑018‑00773‑x
    [Google Scholar]
  22. KalyaneD. Artificial intelligence in the pharmaceutical sector: current scene and future prospect.The Future of Pharmaceutical Product Development and Research. Tekade RakeshK. Elsevier20207310710.1016/B978‑0‑12‑814455‑8.00003‑7
    [Google Scholar]
  23. DuchW. SwaminathanK. MellerJ. Artificial intelligence approaches for rational drug design and discovery.Curr. Pharm. Des.200713141497150810.2174/138161207780765954 17504169
    [Google Scholar]
  24. MakK.K. PichikaM.R. Artificial intelligence in drug development: Present status and future prospects.Drug Discov. Today201924377378010.1016/j.drudis.2018.11.014 30472429
    [Google Scholar]
  25. SellwoodM.A. AhmedM. SeglerM.H.S. BrownN. Artificial intelligence in drug discovery.Future Med. Chem.201810172025202810.4155/fmc‑2018‑0212 30101607
    [Google Scholar]
  26. ReesC. The ethics of artificial intelligence.In: IFIP Advances in Information and Communication TechnologyBoca Raton, FL, USAChapman and Hall/CRC; CRC Press/Taylor & Francis Group2020555556910.1007/978‑3‑030‑64246‑4_5
    [Google Scholar]
  27. QureshiR. IrfanM. GondalT.M. AI in drug discovery and its clinical relevance.Heliyon202397e1757510.1016/j.heliyon.2023.e17575 37396052
    [Google Scholar]
  28. ConsortiumU. UniProt: A hub for protein information.Nucleic Acids Res.201543D1D204D21210.1093/nar/gku989 25348405
    [Google Scholar]
  29. MendezD. GaultonA. BentoA.P. ChEMBL: Towards direct deposition of bioassay data.Nucleic Acids Res.201947D1D930D94010.1093/nar/gky1075 30398643
    [Google Scholar]
  30. Generative AI may accelerate drug discovery for COVID antivirals.Available from: https://healthitanalytics.com/news/generative-ai-may-accelerate-drug-discovery-for-covid-antivirals
  31. PandeyM. FernandezM. GentileF. The transformational role of GPU computing and deep learning in drug discovery.Nat. Mach. Intell.20224321122110.1038/s42256‑022‑00463‑x
    [Google Scholar]
  32. ZhangS. BamakanS.M.H. QuQ. LiS. Learning for personalized medicine: A comprehensive review from a deep learning perspective.IEEE Rev. Biomed. Eng.20191219420810.1109/RBME.2018.2864254 30106692
    [Google Scholar]
  33. Sanchez-LengelingB. Aspuru-GuzikA. Inverse molecular design using machine learning: Generative models for matter engineering.Science2018361640036036510.1126/science.aat2663 30049875
    [Google Scholar]
  34. DaraS. DhamercherlaS. JadavS.S. BabuC.H.M. AhsanM.J. Machine learning in drug discovery: A review.Artif. Intell. Rev.20225531947199910.1007/s10462‑021‑10058‑4 34393317
    [Google Scholar]
  35. ZhangZ. ChenL. ZhongF. Graph neural network approaches for drug-target interactions.Curr. Opin. Struct. Biol.20227310232710.1016/j.sbi.2021.102327 35074533
    [Google Scholar]
  36. GuoJ. Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.PLoS One2024191e029667610.1371/journal.pone.0296676 38232063
    [Google Scholar]
  37. BhatnagarR. SardarS. BeheshtiM. PodichettyJ.T. How can natural language processing help model informed drug development?: A review.JAMIA Open202252ooac04310.1093/jamiaopen/ooac043 35702625
    [Google Scholar]
  38. HuW. Reinforcement learning of molecule optimization with bayesian neural networks.Comput. Mol. Biosci.2021114698310.4236/cmb.2021.114005
    [Google Scholar]
  39. How do vaccines work?Available from: https://www.who.int/news-room/feature-stories/detail/how-do-vaccines-work Accessed on 01.02.2024.
  40. KakavandiS ZareI VaezJalaliM Structural and non-structural proteins in SARS-CoV-2: potential aspects to COVID-19 treatment or prevention of progression of related diseases.Cell Commun. Signal.202321111010.1186/s12964‑023‑01104‑5 37189112
    [Google Scholar]
  41. CarabelliA.M. PeacockT.P. ThorneL.G. SARS-CoV-2 variant biology: Immune escape, transmission and fitness.Nat. Rev. Microbiol.202321316217710.1038/s41579‑022‑00841‑7 36653446
    [Google Scholar]
  42. SetteA. CrottyS. Immunological memory to SARS‐CoV ‐2 infection and COVID ‐19 vaccines.Immunol. Rev.20223101274610.1111/imr.13089 35733376
    [Google Scholar]
  43. Keshavarzi ArshadiA. WebbJ. SalemM. Artificial intelligence for covid-19 drug discovery and vaccine development.Frontiers in Artificial Intelligence202036510.3389/frai.2020.00065 33733182
    [Google Scholar]
  44. BeckB.R. ShinB. ChoiY. ParkS. KangK. Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model.bioRxiv202010.1101/2020.01.31.929547
    [Google Scholar]
  45. WallachI DzambaM HeifetsA. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery.arxiv2020
    [Google Scholar]
  46. KimJ. ZhangJ. ChaY. KolitzS. FuntJ. Escalante ChongR. Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease–2019 (COVID-19).ChemRxiv202010.26434/chemrxiv.12037416.v1
    [Google Scholar]
  47. ChoudharyS. MalikY.S. TomarS. Identification of SARS-CoV-2 cell entry inhibitors by drug repurposing using in silico structure-based virtual screening approach.ChemRxiv202010.26434/chemrxiv.12005988.v2
    [Google Scholar]
  48. Tahir ul Qamar M, Alqahtani SM, Alamri MA, Chen LL. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants.J. Pharm. Anal.202010431331910.1016/j.jpha.2020.03.009 32296570
    [Google Scholar]
  49. HuB. GuoH. ZhouP. ShiZ.L. Characteristics of SARS-CoV-2 and COVID-19.Nat. Rev. Microbiol.202119314115410.1038/s41579‑020‑00459‑7 33024307
    [Google Scholar]
  50. Using AI to design RNA-based medicinesAvailable from: https://www.ddw-online.com/using-ai-to-design-rna-based-medicines-22939-202304/ Accessed on: 03.02.2024.
  51. MiaoZ. TiduA. ErianiG. MartinF. Secondary structure of the SARS-CoV-2 5′-UTR.RNA Biol.202118444745610.1080/15476286.2020.1814556 32965173
    [Google Scholar]
  52. YangD. LeibowitzJ.L. The structure and functions of coronavirus genomic 3′ and 5′ ends.Virus Res.201520612013310.1016/j.virusres.2015.02.025 25736566
    [Google Scholar]
  53. ZhaoJ. QiuJ. AryalS. HackettJ. WangJ. The RNA architecture of the SARS-CoV-2 3′-untranslated region.Viruses20201212147310.3390/v12121473 33371200
    [Google Scholar]
  54. NaqviA.A.T. FatimaK. MohammadT. Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach.Biochim. Biophys. Acta Mol. Basis Dis.202018661016587810.1016/j.bbadis.2020.165878 32544429
    [Google Scholar]
  55. SaramagoM. BárriaC. CostaV.G. New targets for drug design: Importance of nsp14/nsp10 complex formation for the 3′‐5′ exoribonucleolytic activity on SARS‐CoV‐2.FEBS J.2021288175130514710.1111/febs.15815 33705595
    [Google Scholar]
  56. BouvetM. LugariA. PosthumaC.C. Coronavirus Nsp10, a critical co-factor for activation of multiple replicative enzymes.J. Biol. Chem.201428937257832579610.1074/jbc.M114.577353 25074927
    [Google Scholar]
  57. SahaO. IslamI. ShatadruR.N. RakhiN.N. HossainM.S. RahamanM.M. Temporal landscape of mutational frequencies in SARS-CoV-2 genomes of Bangladesh: Possible implications from the ongoing outbreak in Bangladesh.Virus Genes202157541342510.1007/s11262‑021‑01860‑x 34251592
    [Google Scholar]
  58. AnandN.M. LiyaD.H. PradhanA.K. A comprehensive SARS-CoV-2 genomic analysis identifies potential targets for drug repurposing.PLoS One2021163e024855310.1371/journal.pone.0248553 33735271
    [Google Scholar]
  59. ZhuW. ChenC.Z. GorshkovK. XuM. LoD.C. ZhengW. RNA-dependent RNA polymerase as a target for covid-19 drug discovery.SLAS Discov.202025101141115110.1177/2472555220942123 32660307
    [Google Scholar]
  60. JurtzV. PaulS. AndreattaM. NielsenH. PetersB. NielsenJ. NetMHCpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and mass spectrometry data.Nucleic Acids Res.201745D1D742D74710.1093/nar/gkx1034
    [Google Scholar]
  61. OwensR.J. WuH. StanislausK. LabordeR. ZhuH. Diaz-MunozM.D. Predicting antibody class-specific B-cell epitopes using a high-throughput pipelined approach.Front. Immunol.20211270085410.3389/fimmu.2021.700854
    [Google Scholar]
  62. ZhangJ.Y. HeF. LiY. ZhaoH. WangC.X. LiY. DeepVacPred: Sequence-based deep learning for identifying vaccine design targets.PLOS Comput. Biol.2022189e101005310.1371/journal.pcbi.1010053 36149894
    [Google Scholar]
  63. LarsenJ. LundO. NielsenM. NeumaierB. Improved method for predicting linear B-cell epitopes.Immunome Res.200621210.1186/1745‑7580‑2‑2 16635264
    [Google Scholar]
  64. BuiH.H. SidneyJ. LiW. FusseyS.Y. SetteA. A computational method for focusing large-scale identification of human immunodeficiency virus type 1 CD8+ T cell epitopes.Hum. Immunol.200566328329410.1016/j.humimmu.2004.10.019
    [Google Scholar]
  65. JensenK.K. NielsenH. LundegaardC. PetersB. MHC CTLepitope pan-specificity prediction for peptide vaccine design.Sci. Rep.201881678610.1038/s41598‑018‑25057‑4 29691448
    [Google Scholar]
  66. SahaS. RaghavaG.P. BcePred: Prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties.Proteins200454339440210.1002/prot.2004.10384 14747988
    [Google Scholar]
  67. SaagM. Wonder of wonders, miracle of miracles: The unprecedented speed of COVID-19 science.Physiol. Rev.202210231569157710.1152/physrev.00010.2022 35446679
    [Google Scholar]
  68. NicholA.A. AntierensA. Ethics of emerging infectious disease outbreak responses: Using Ebola virus disease as a case study of limited resource allocation.PLoS One2021162e024632010.1371/journal.pone.0246320 33529237
    [Google Scholar]
  69. GhoshA. Larrondo-PetrieM.M. PavlovicM. Revolutionizing vaccine development for COVID-19: A review of AI-based approaches.Information2023141266510.3390/info14120665
    [Google Scholar]
  70. ZhangJ. TaoA. Antigenicity, immunogenicity.Translational Bioinformatics2015817518610.1007/978‑94‑017‑7444‑4_11
    [Google Scholar]
  71. Biologists train AI to generate medicines and vaccinesAvailable from: https://newsroom.uw.edu/news-releases/biologists-train-ai-generate-medicines-and-vaccines Accessed on 01.02.2024.
  72. ThomasS. AbrahamA. BaldwinJ. PiplaniS. PetrovskyN. Artificial intelligence in vaccine and drug design.Methods Mol. Biol.2022241013114610.1007/978‑1‑0716‑1884‑4_6 34914045
    [Google Scholar]
  73. McCaffreyP. Artificial intelligence for vaccine design.Methods Mol. Biol.2022241231310.1007/978‑1‑0716‑1892‑9_1 34918238
    [Google Scholar]
  74. Using AI to create a vaccine revolutionAvailable from: https://www.nature.com/articles/d43747-023-00051-x Accessed on 01.02.2024.
  75. SubbaraoK. The success of SARS-CoV-2 vaccines and challenges ahead.Cell Host Microbe20212971111112310.1016/j.chom.2021.06.016 34265245
    [Google Scholar]
  76. PaddaI.S. ParmarM. COVID (SARS-CoV-2) Vaccine.In: StatPearls.Treasure Island, FLStatPearls Publishing2024
    [Google Scholar]
  77. FortnerA. SchumacherD. First COVID-19 vaccines receiving the US FDA and EMA emergency use authorization.Discoveries202191e12210.15190/d.2021.1 33969180
    [Google Scholar]
  78. Applying the latest digital technology to optimize covid-19 vaccine efforts.Available from: https://www.pfizer.com/sites/default/files/investors/financial_reports/annual_reports/2021/story/latest-digital-technology-to-covid-vaccine-efforts/ [Accessed on 04.02.2024].
  79. HaqH.N. KhanH. ChaudhryH. Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273) COVID-19 mRNA vaccines and hypersensitivity reactions.J. Natl. Med. Assoc.2022114660161210.1016/j.jnma.2022.08.003 36511275
    [Google Scholar]
  80. HoganM.J. PardiN. mRNA Vaccines in the COVID-19 Pandemic and Beyond.Annu. Rev. Med.2022731173910.1146/annurev‑med‑042420‑112725 34669432
    [Google Scholar]
  81. AlejoJ.L. MitchellJ. ChiangT.P.Y. Predicting a positive antibody response after 2 SARS-CoV-2 mRNA vaccines in transplant recipients: A machine learning approach with external validation.Transplantation202210610e452e46010.1097/TP.0000000000004259 35859275
    [Google Scholar]
  82. How gen AI is helping pfizer change healthcare narrative.Available from: https://www.cio.inc/how-gen-ai-helping-pfizer-change-healthcare-narrative-a-23786 [Accessed on 04.02.2024].
  83. GastA. COVID showed AI’s potential for accelerating progress, but we must ensure AI is employed in ways that are trusted.Available from: https://theprint.in/tech/ai-helped-moderna-speed-up-covid-vaccine-development-now-it-can-help-climate-too/955061/ 2022
  84. KyriakidisN.C. López-CortésA. GonzálezE.V. GrimaldosA.B. PradoE.O. SARS-CoV-2 vaccines strategies: A comprehensive review of phase 3 candidates.NPJ Vaccines2021612810.1038/s41541‑021‑00292‑w 33619260
    [Google Scholar]
  85. The Sinovac-CoronaVac COVID-19 vaccine: What you need to know.Available from: https://www.who.int/news-room/feature-stories/detail/the-sinovac-covid-19-vaccine-what-you-need-to-know [Accessed on 04.02.2024].
  86. FirouzabadiN. GhasemiyehP. MoradishooliF. Mohammadi-SamaniS. Update on the effectiveness of COVID-19 vaccines on different variants of SARS-CoV-2.Int. Immunopharmacol.202311710996810.1016/j.intimp.2023.109968 37012880
    [Google Scholar]
  87. KumarR. VeerK. How artificial intelligence and internet of things can aid in the distribution of COVID-19 vaccines.Diabetes Metab. Syndr.20211531049105010.1016/j.dsx.2021.04.021 33941496
    [Google Scholar]
  88. SachdevaS. How can India devise a seamless vaccination program using AI & IoT?Available from: https://www.geospatialworld.net/blogs/how-can-india-devise-a-seamless-vaccination-program-using-ai-iot/ 2021
  89. SinghP. UjjainiyaR. PrakashS. A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.Comput. Biol. Med.202214610541910.1016/j.compbiomed.2022.105419 35483225
    [Google Scholar]
  90. Sinopharm (Beijing): Covilo.Available from: https://covid19.trackvaccines.org/vaccines/5/ [Accessed on 05.02.2024].
  91. LedfordH.J. One-shot COVID vaccine offers hope for faster protection.Nature202110.1038/d41586‑021‑00119‑7
    [Google Scholar]
  92. AI in drug development: Janssen exploring potential of AI in everything from target discovery to clinical trials.Available from: https://www.drugdiscoverytrends.com/ai-drug-development-janssen-target-discovery-and-beyond/ Accessed on: 05.02.2024.
  93. AstraZeneca ChAdOx1-S/nCoV-19 [recombinant], COVID-19 vaccine.Available from: https://www.who.int/publications/m/item/chadox1-s-recombinant-covid-19-vaccine [Accessed on: 05.02.2024].
  94. SachdevaS. How can India devise a seamless vaccination program using AI & IoT?Available from: https://www.geospatialworld.net/blogs/how-can-india-devise-a-seamless-vaccination-program-using-ai-iot/ [Accessed on:05.02.2024].2021
  95. COVID-19 vaccine: What you need to know.Available from: https://www.who.int/news-room/feature-stories/detail/the-oxford-astrazeneca-covid-19-vaccine-what-you-need-to-know [Accessed on:05.02.2024].
  96. SputkinV. Available from: https://sputnikvaccine.com/ [Accessed on:05.02.2024].
  97. How gamaleya’s vaccine works. Available from: https://www.nytimes.com/interactive/2021/health/gamaleya-covid-19-vaccine.html [Accessed on:05.02.2024].
  98. GeersD ShamierMC BogersS SARS-CoV-2 variants of concern partially escape humoral but not T-cell responses in COVID-19 convalescent donors and vaccinees.Sci Immunol2021659eabj175010.1126/sciimmunol.abj1750
    [Google Scholar]
  99. KumarR. SrivastavaY. MuthuramalingamP. Understanding mutations in human SARS-CoV-2 spike glycoprotein: A systematic review & meta-analysis.Viruses202315485610.3390/v15040856 37112836
    [Google Scholar]
  100. KhanA. ZiaT. SulemanM. Higher infectivity of the SARS‐CoV‐2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data.J. Cell. Physiol.2021236107045705710.1002/jcp.30367 33755190
    [Google Scholar]
  101. HarveyW.T. CarabelliA.M. JacksonB. SARS-CoV-2 variants, spike mutations and immune escape.Nat. Rev. Microbiol.202119740942410.1038/s41579‑021‑00573‑0 34075212
    [Google Scholar]
  102. ChenJ. WangR. GilbyN.B. WeiG.W. Omicron variant (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance.J. Chem. Inf. Model.202262241242210.1021/acs.jcim.1c01451 34989238
    [Google Scholar]
  103. Blanco-GonzálezA. CabezónA. Seco-GonzálezA. The role of AI in drug discovery: Challenges, opportunities, and strategies.Pharmaceuticals202316689110.3390/ph16060891 37375838
    [Google Scholar]
  104. VoraL.K. GholapA.D. JethaK. ThakurR.R.S. SolankiH.K. ChavdaV.P. Artificial intelligence in pharmaceutical technology and drug delivery design.Pharmaceutics2023157191610.3390/pharmaceutics15071916 37514102
    [Google Scholar]
  105. AkhtarM. KraemerM.U.G. GardnerL.M. A dynamic neural network model for predicting risk of Zika in real time.BMC Med.201917117110.1186/s12916‑019‑1389‑3 31474220
    [Google Scholar]
  106. NaudéW. Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls.AI Soc.202035376176510.1007/s00146‑020‑00978‑0 32346223
    [Google Scholar]
  107. FastE. AltmanR.B. ChenB. Potential T-cell and B[1]cell epitopes of 2019-nCoVbioRxiv202002.19.955484.10.1101/2020.02.19.955484
    [Google Scholar]
  108. HarrerS. ShahP. AntonyB. HuJ. Artificial intelligence for clinical trial design.Trends Pharmacol. Sci.201940857759110.1016/j.tips.2019.05.005 31326235
    [Google Scholar]
  109. KulkarniR FershtP CushmanD Pfizer’s vaccine proves the Power of Emerging Tech when the Burning Plat[1]form is Red HotAvailable from: https://www.hfsresearch.com/research/pfizers-vaccine-proves-the-power-of-emerging[1]tech-when-the-burning-platform-is-red-hot/ 2021
  110. WaltzE. AI takes its best shot: What AI can—and can’t—do in the race for a coronavirus vaccine.IEEE Spectrum.20205710246710.1109/MSPEC.2020.9205545
    [Google Scholar]
  111. PiccialliF. di ColaV.S. GiampaoloF. CuomoS. The role of artificial intelligence in fighting the COVID-19 pandemic.Inf. Syst. Front.20212361467149710.1007/s10796‑021‑10131‑x 33935585
    [Google Scholar]
  112. GhoshD. GhoshS. SinghaP.S. Mechanism of infection of SARS-CoV-2 and gender based differential impacts.Indian J. Clin. Anat. Physiol.202410427828010.18231/j.ijcap.2023.061
    [Google Scholar]
  113. SinghaPS GhoshR GhoshD Immunomodulatory phytocompounds from Withania somnifera (L) Dunal (Ashwagandha) against COVID-19.Trends in immunotherapy202481114
    [Google Scholar]
  114. SinghaPS JanaAK GhoshD FirdausSB Antiviral phytochemicals from adhatoda vasica, a traditional medicinal plant of India.Published in plant a valuable resource of sustainable agriculture, food and medicine.2021212
    [Google Scholar]
  115. GhoshS. SinghaP. DasL.K. GhoshD. Systematic review on major antiviral phytocompounds from common medicinal plants against SARS-CoV-2.Med. Chem.20242010.2174/0115734064262843231120051452 38317467
    [Google Scholar]
/content/journals/covid/10.2174/0126667975309811240530114325
Loading
/content/journals/covid/10.2174/0126667975309811240530114325
Loading

Data & Media loading...

Supplements

PRISMA checklist is available on the publisher's website along with the published article.


  • Article Type:
    Review Article
Keyword(s): Artificial intelligence; COVID-19; drug; SARS-CoV-2; support vector machine; vaccine
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test