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2000
Volume 20, Issue 7
  • ISSN: 1574-8855
  • E-ISSN: 2212-3903

Abstract

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating early-stage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery.

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2025-12-03
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References

  1. HametP. TremblayJ. Artificial intelligence in medicine.Metabolism201769S36S4010.1016/j.metabol.2017.01.011 28126242
    [Google Scholar]
  2. HassanzadehP. AtyabiF. DinarvandR. The significance of artificial intelligence in drug delivery system design.Adv. Drug Deliv. Rev.2019151-15216919010.1016/j.addr.2019.05.001 31071378
    [Google Scholar]
  3. SunY. PengY. ChenY. ShuklaA.J. Application of artificial neural networks in the design of controlled release drug delivery systems.Adv. Drug Deliv. Rev.20035591201121510.1016/S0169‑409X(03)00119‑4 12954199
    [Google Scholar]
  4. Al-KharusiG. DunneN.J. LittleS. LevingstoneT.J. The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research.Bioengineering202291056110.3390/bioengineering9100561 36290529
    [Google Scholar]
  5. HesslerG. BaringhausK.H. Artificial intelligence in drug design.Molecules20182310252010.3390/molecules23102520 30279331
    [Google Scholar]
  6. SarkarC. DasB. RawatV.S. Artificial intelligence and machine learning technology driven modern drug discovery and development.Int. J. Mol. Sci.2023243202610.3390/ijms24032026 36768346
    [Google Scholar]
  7. 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]
  8. Jiménez-LunaJ. GrisoniF. SchneiderG. Drug discovery with explainable artificial intelligence.Nat. Mach. Intell.202021057358410.1038/s42256‑020‑00236‑4
    [Google Scholar]
  9. SelvarajC. ChandraI. SinghS.K. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries.Mol. Divers.20222631893191310.1007/s11030‑021‑10326‑z 34686947
    [Google Scholar]
  10. YangX. WangY. ByrneR. SchneiderG. YangS. Concepts of artificial intelligence for computer-assisted drug discovery.Chem. Rev.201911918105201059410.1021/acs.chemrev.8b00728 31294972
    [Google Scholar]
  11. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery.Mol. Divers.20212531315136010.1007/s11030‑021‑10217‑3 33844136
    [Google Scholar]
  12. IwataH. Application of in silico technologies for drug target discovery and pharmacokinetic analysis.Chem. Pharm. Bull.202371639840510.1248/cpb.c22‑00638 37258192
    [Google Scholar]
  13. ChanH.C.S. ShanH. DahounT. VogelH. YuanS. Advancing drug discovery via artificial intelligence.Trends Pharmacol. Sci.201940859260410.1016/j.tips.2019.06.004 31320117
    [Google Scholar]
  14. ChengX. XieQ. SunY. Advances in nanomaterial-based targeted drug delivery systems.Front. Bioeng. Biotechnol.202311117715110.3389/fbioe.2023.1177151 37122851
    [Google Scholar]
  15. 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]
  16. KavasidisI. LallasE. GerogiannisV.C. CharitouT. KarageorgosA. Predictive maintenance in pharmaceutical manufacturing lines using deep transformers.Procedia Comput. Sci.202322057658310.1016/j.procs.2023.03.073
    [Google Scholar]
  17. BagherianM. SabetiE. WangK. SartorM.A. Nikolovska-ColeskaZ. NajarianK. Machine learning approaches and databases for prediction of drug–target interaction: A survey paper.Brief. Bioinform.202122124726910.1093/bib/bbz157 31950972
    [Google Scholar]
  18. ZhangZ. LiZ. YuW. LiK. XieZ. Development of a biomedical micro/nano robot for drug delivery.J. Nanosci. Nanotechnol.20151543126312910.1166/jnn.2015.9643 26353548
    [Google Scholar]
  19. KumarY. KoulA. SinglaR. IjazM.F. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda.J. Ambient Intell. Humaniz. Comput.20231478459848610.1007/s12652‑021‑03612‑z 35039756
    [Google Scholar]
  20. ChapmanA.B. PetersonK.S. AlbaP.R. DuVallS.L. PattersonO.V. Detecting adverse drug events with rapidly trained classification models.Drug Saf.201942114715610.1007/s40264‑018‑0763‑y 30649737
    [Google Scholar]
  21. ElkinM.E. ZhuX. Predictive modeling of clinical trial terminations using feature engineering and embedding learning.Sci. Rep.2021111344610.1038/s41598‑021‑82840‑x 33568706
    [Google Scholar]
  22. GaoW. WangC. LiQ. Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip.Front. Bioeng. Biotechnol.20221098569210.3389/fbioe.2022.985692 36172022
    [Google Scholar]
  23. HoD. WangP. KeeT. Artificial intelligence in nanomedicine.Nanoscale Horiz.20194236537710.1039/C8NH00233A 32254089
    [Google Scholar]
  24. Bermejillo BarreraM.D. Franco-MartínezF. Díaz LantadaA. Artificial intelligence aided design of tissue engineering scaffolds employing virtual tomography and 3d convolutional neural networks.Materials20211418527810.3390/ma14185278 34576503
    [Google Scholar]
  25. BhinderB. GilvaryC. MadhukarN.S. ElementoO. Artifi Cial intelligence in cancer research and precision medicine.Cancer Discov.202111490091510.1158/2159‑8290.CD‑21‑0090 33811123
    [Google Scholar]
  26. BiW.L. HosnyA. SchabathM.B. Artificial intelligence in cancer imaging: Clinical challenges and applications.CA Cancer J. Clin.201969212715710.3322/caac.21552 30720861
    [Google Scholar]
  27. ChenZ.H. LinL. WuC.F. LiC.F. XuR.H. SunY. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.Cancer Commun.202141111100111510.1002/cac2.12215 34613667
    [Google Scholar]
  28. AdirO. PoleyM. ChenG. Integrating artificial intelligence and nanotechnology for precision cancer medicine.Adv. Mater.20203213190198910.1002/adma.201901989 31286573
    [Google Scholar]
  29. ArunaR D KSSurendran S D etal An enhancement on convolutional artificial intelligent based diagnosis for skin disease using nanotechnology sensors.Comput. Intell. Neurosci.202220221610.1155/2022/9539503 35832245
    [Google Scholar]
  30. LeT.C. MuletX. BurdenF.R. WinklerD.A. Predicting the complex phase behavior of self-assembling drug delivery nanoparticles.Mol. Pharm.20131041368137710.1021/mp3006402 23464802
    [Google Scholar]
  31. SinghA.V. AnsariM.H.D. RosenkranzD. Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine.Adv. Healthc. Mater.2020917190186210.1002/adhm.201901862 32627972
    [Google Scholar]
  32. WilsonB. KmG. Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment.Nanomedicine202015543343510.2217/nnm‑2019‑0366 31997697
    [Google Scholar]
  33. IbrićS. JovanovićM. DjurićZ. ParojčićJ. SolomunL. The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit® RS PO as matrix substance.J. Control. Release2002822-321322210.1016/S0168‑3659(02)00044‑5 12175738
    [Google Scholar]
  34. SinghA.V. VarmaM. LauxP. Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: A comprehensive review.Arch. Toxicol.202397496397910.1007/s00204‑023‑03471‑x 36878992
    [Google Scholar]
  35. KomotoY. OhshiroT. YoshidaT. Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.Sci. Rep.20201011124410.1038/s41598‑020‑68236‑3 32647343
    [Google Scholar]
  36. LinZ. ChouW.C. ChengY.H. HeC. Monteiro-RiviereN.A. RiviereJ.E. Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches.Int. J. Nanomedicine2022171365137910.2147/IJN.S344208 35360005
    [Google Scholar]
  37. PolizuS. SavadogoO. PoulinP. YahiaL.H. Applications of carbon nanotubes-based biomaterials in biomedical nanotechnology.J. Nanosci. Nanotechnol.2006671883190410.1166/jnn.2006.197 17025102
    [Google Scholar]
  38. SachaG.M. VaronaP. Artificial intelligence in nanotechnology.Nanotechnology2013244545200210.1088/0957‑4484/24/45/452002 24121558
    [Google Scholar]
  39. HeS. LeanseL.G. FengY. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases.Adv. Drug Deliv. Rev.202117811392210.1016/j.addr.2021.113922 34461198
    [Google Scholar]
  40. MitsalaA. TsalikidisC. PitiakoudisM. SimopoulosC. TsarouchaA.K. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era.Curr. Oncol.20212831581160710.3390/curroncol28030149 33922402
    [Google Scholar]
  41. EgorovE. PietersC. Korach-RechtmanH. ShkloverJ. SchroederA. Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems.Drug Deliv. Transl. Res.202111234535210.1007/s13346‑021‑00929‑2 33585972
    [Google Scholar]
  42. TangJ. HuangN. ZhangX. Aptamer-conjugated PEGylated quantum dots targeting epidermal growth factor receptor variant III for fluorescence imaging of glioma.Int. J. Nanomedicine2017123899391110.2147/IJN.S133166 28579776
    [Google Scholar]
  43. YacoubA.S. AmmarH.O. IbrahimM. MansourS.M. El HoffyN.M. Artificial intelligence-assisted development of in situ forming nanoparticles for arthritis therapy via intra-articular delivery.Drug Deliv.20222911423143610.1080/10717544.2022.2069882 35532141
    [Google Scholar]
  44. ChenY. McCallT.W. BaichwalA.R. MeyerM.C. The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms.J. Control. Release1999591334110.1016/S0168‑3659(98)00171‑0 10210720
    [Google Scholar]
  45. 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]
  46. LauH.J. LimC.H. FooS.C. TanH.S. The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.Curr. Genet.202167342142910.1007/s00294‑021‑01156‑5 33585980
    [Google Scholar]
  47. GardnerM.W. DorlingS.R. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences.Atmos. Environ.19983214-152627263610.1016/S1352‑2310(97)00447‑0
    [Google Scholar]
  48. ArulsudarN. SubramanianN. MurthyR.S.R. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.J. Pharm. Pharm. Sci.200582243258
    [Google Scholar]
  49. SinghA.V. ChandrasekarV. JanapareddyP. Emerging application of nanorobotics and artificial intelligence to cross the BBB: Advances in design, controlled maneuvering, and targeting of the barriers.ACS Chem. Neurosci.202112111835185310.1021/acschemneuro.1c00087 34008957
    [Google Scholar]
  50. SinghA.V. AnsariM.H.D. LauxP. LuchA. Micro-nanorobots: Important considerations when developing novel drug delivery platforms.Expert Opin. Drug Deliv.201916111259127510.1080/17425247.2019.1676228 31580731
    [Google Scholar]
  51. KolluriS. LinJ. LiuR. ZhangY. ZhangW. Machine learning and artificial intelligence in pharmaceutical research and development: A review.AAPS J.20222411910.1208/s12248‑021‑00644‑3 34984579
    [Google Scholar]
  52. BaskinI.I. WinklerD. TetkoI.V. A renaissance of neural networks in drug discovery.Expert Opin. Drug Discov.201611878579510.1080/17460441.2016.1201262 27295548
    [Google Scholar]
  53. GodavarthyS.S. YerramsettyK.M. RachakondaV.K. Design of improved permeation enhancers for transdermal drug delivery.J. Pharm. Sci.200998114085409910.1002/jps.21940 19697392
    [Google Scholar]
  54. YuanS. AjamH. SinnahZ.A.B. The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review.Ecotoxicol. Environ. Saf.202326011506610.1016/j.ecoenv.2023.115066 37262969
    [Google Scholar]
  55. TakayamaK. MorvaA. FujikawaM. HattoriY. ObataY. NagaiT. Formula optimization of theophylline controlled-release tablet based on artificial neural networks.J. Control. Release200068217518610.1016/S0168‑3659(00)00248‑0
    [Google Scholar]
  56. RezayiS. R Niakan Kalhori S, Saeedi S. Effectiveness of artificial intelligence for personalized medicine in neoplasms: A systematic review.BioMed Res. Int.2022202213410.1155/2022/7842566 35434134
    [Google Scholar]
  57. SolankiS.L. PandrowalaS. NayakA. BhandareM. AmbulkarR.P. ShrikhandeS.V. Artificial intelligence in perioperative management of major gastrointestinal surgeries.World J. Gastroenterol.202127212758277010.3748/wjg.v27.i21.2758 34135552
    [Google Scholar]
  58. FletcherM. BiglarbegianM. NeethirajanS. Intelligent system design for bionanorobots in drug delivery.Cancer Nanotechnol.201344-511712510.1007/s12645‑013‑0044‑5 26069507
    [Google Scholar]
  59. ChenP.C. LuY.R. KangY.N. ChangC.C. The accuracy of artificial intelligence in the endoscopic diagnosis of early gastric cancer: Pooled analysis study.J. Med. Internet Res.2022245e2769410.2196/27694 35576561
    [Google Scholar]
  60. Virgolino da Silva LuzG. Vânio Gomes BarrosK. Vladimir Calixto de AraújoF. Barbosa da SilvaG. Augusto Ferreira da SilvaP. Claudia Iquize CondoriR. Nanorobotics in drug delivery systems for treatment of cancer: A review.J. Mater. Sci. Eng. A20166310.17265/2161‑6213/2016.5‑6.005
    [Google Scholar]
  61. Schmidt-ErfurthU. SadeghipourA. GerendasB.S. WaldsteinS.M. BogunovićH. Artificial intelligence in retina.Prog. Retin. Eye Res.20186712910.1016/j.preteyeres.2018.07.00430076935
    [Google Scholar]
  62. RainierMallol FadzilahKamaludin RohaniAhmad Artificial intelligence model as predictor for dengue outbreaks.Malays. J. Public Health Med.201919210310810.37268/mjphm/vol.19/no.2/art.176
    [Google Scholar]
  63. KhanM.A. AbidiW.U.H. Al GhamdiM.A. AlmotiriS.H. SaqibS. AlyasT. Forecast the influenza pandemic using machine learning.Comput. Mater. Continua202166133134010.32604/cmc.2020.012148
    [Google Scholar]
  64. KhanM.T. KaushikA.C. JiL. MalikS.I. AliS. WeiD.Q. Artificial neural networks for prediction of tuberculosis disease.Front. Microbiol.20191039510.3389/fmicb.2019.00395 30886608
    [Google Scholar]
  65. 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]
  66. CarrerasJ. KikutiY.Y. MiyaokaM. Artificial intelligence analysis of the gene expression of follicular lymphoma predicted the overall survival and correlated with the immune microenvironment response signatures.Machine Learning and Knowledge Extraction20202464767110.3390/make2040035
    [Google Scholar]
  67. HasaniM. MoloudiM. EmamiF. Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component–artificial neural network models.Anal. Biochem.20073701687610.1016/j.ab.2007.06.025 17662683
    [Google Scholar]
  68. DhimanS. AroraG. RastogiV. SinghT.G. Recent advances in nano-formulations for ophthalmic drug delivery.J Pharm Sci Res202012213223
    [Google Scholar]
  69. BhattamisraS.K. BanerjeeP. GuptaP. MayurenJ. PatraS. CandasamyM. Artificial intelligence in pharmaceutical and healthcare research.Big Data and Cognitive Computing2023711010.3390/bdcc7010010
    [Google Scholar]
  70. SardariS. KohanzadH. GhavamiG. Artificial neural network modeling of antimycobacterial chemical space to introduce efficient descriptors employed for drug design.Chemom. Intell. Lab. Syst.201413015115810.1016/j.chemolab.2013.09.011
    [Google Scholar]
  71. SahuA. MishraJ. KushwahaN. Artificial intelligence (AI) in drugs and pharmaceuticals.Comb. Chem. High Throughput Screen.202225111818183710.2174/1386207325666211207153943 34875986
    [Google Scholar]
  72. YildirimO. GottwaldM. SchülerP. MichelM.C. Opportunities and challenges for drug development: Public-private partnerships, adaptive designs and big data.Front. Pharmacol.2016746110.3389/fphar.2016.00461 27999543
    [Google Scholar]
  73. HeldC.M. RoyR.J. Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: Validation on a model.IEEE Trans. Biomed. Eng.199542437138510.1109/10.376130 7729836
    [Google Scholar]
  74. HillC. AmodeoA. JosephJ.V. PatelH.R.H. Nano- and microrobotics: How far is the reality?Expert Rev. Anticancer Ther.20088121891189710.1586/14737140.8.12.1891 19046109
    [Google Scholar]
  75. MonacoI. CamoraniS. ColecchiaD. Aptamer functionalization of nanosystems for glioblastoma targeting through the blood–brain barrier.J. Med. Chem.201760104510451610.1021/acs.jmedchem.7b00527 28471660
    [Google Scholar]
  76. MulvihillJ.J.E. CunnaneE.M. RossA.M. DuskeyJ.T. TosiG. GrabruckerA.M. Drug delivery across the blood-brain barrier: Recent advances in the use of nanocarriers.Nanomedicine202015220521410.2217/nnm‑2019‑0367 31916480
    [Google Scholar]
  77. SharmaG. SharmaA.R. LeeS.S. BhattacharyaM. NamJ.S. ChakrabortyC. Advances in nanocarriers enabled brain targeted drug delivery across blood brain barrier.Int. J. Pharm.201955936037210.1016/j.ijpharm.2019.01.056 30721725
    [Google Scholar]
  78. HuangJ.W. RoyR.J. Multiple-drug hemodynamic control using fuzzy decision theory.IEEE Trans. Biomed. Eng.199845221322810.1109/10.661269 9473844
    [Google Scholar]
  79. PetrovićJ. IbrićS. BetzG. ĐurićZ. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.Int. J. Pharm.20124281-2576710.1016/j.ijpharm.2012.02.031 22402474
    [Google Scholar]
  80. PatelG.M. PatelG.C. PatelR.B. PatelJ.K. PatelM. Nanorobot: A versatile tool in nanomedicine.J. Drug Target.2006142636710.1080/10611860600612862 16608733
    [Google Scholar]
  81. MackayB.S. MarshallK. Grant-JacobJ.A. The future of bone regeneration: Integrating AI into tissue engineering.Biomed. Phys. Eng. Express20217505200210.1088/2057‑1976/ac154f 34271556
    [Google Scholar]
  82. GillS.S. XuM. OttavianiC. PatrosP. BahsoonR. ShaghaghiA. AI for next generation computing: Emerging trends and future directions.arXiv220304159202210.1016/j.iot.2022.100514
    [Google Scholar]
  83. XiaJ. JinH. LiuZ. ZhangL. WangX.S. An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.J. Chem. Inf. Model.20145451433145010.1021/ci500062f 24749745
    [Google Scholar]
  84. CarpenterK.A. CohenD.S. JarrellJ.T. HuangX. Deep learning and virtual drug screening.Future Med. Chem.201810212557256710.4155/fmc‑2018‑0314 30288997
    [Google Scholar]
  85. PuvianiM. FreiR. Self-management for cloud computing.2013 Science and Information Conference, London, UK, October 7-9, 2013, pp. 940-946
    [Google Scholar]
  86. KreimeyerK. FosterM. PandeyA. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.J. Biomed. Inform.201773142910.1016/j.jbi.2017.07.012 28729030
    [Google Scholar]
  87. HarrisonC.J. Sidey-GibbonsC.J. Machine learning in medicine: A practical introduction to natural language processing.BMC Med. Res. Methodol.202121115810.1186/s12874‑021‑01347‑1 34332525
    [Google Scholar]
  88. JohnsonK.B. WeiW.Q. WeeraratneD. Precision medicine, AI, and the future of personalized health care.Clin. Transl. Sci.2021141869310.1111/cts.12884 32961010
    [Google Scholar]
  89. HassanzadehP. AtyabiF. DinarvandR. Creation of nanorobots: Both state-of-the-science and state-of-the-art.Biomedical Reviews2017270192610.14748/bmr.v27.2109
    [Google Scholar]
  90. ColomboS. Applications of artificial intelligence in drug delivery and pharmaceutical development.Artificial Intelligence in Healthcare20208511610.1016/B978‑0‑12‑818438‑7.00004‑6
    [Google Scholar]
  91. 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]
  92. Jiménez-LunaJ. GrisoniF. WeskampN. SchneiderG. Artificial intelligence in drug discovery: Recent advances and future perspectives.Expert Opin. Drug Discov.202116994995910.1080/17460441.2021.1909567 33779453
    [Google Scholar]
  93. KumarP. SinhaR. ShuklaP. Artificial intelligence and synthetic biology approaches for human gut microbiome.Crit. Rev. Food Sci. Nutr.20226282103212110.1080/10408398.2020.1850415 33249867
    [Google Scholar]
  94. LaterzaL. PutignaniL. SettanniC.R. Ecology and machine learning-based classification models of gut microbiota and inflammatory markers may evaluate the effects of probiotic supplementation in patients recently recovered from COVID-19.Int. J. Mol. Sci.2023247662310.3390/ijms24076623 37047594
    [Google Scholar]
  95. BennerS.A. SismourA.M. Synthetic biology.Nat. Rev. Genet.20056753354310.1038/nrg1637 15995697
    [Google Scholar]
  96. RossK. Psychobiotics: Are they the future intervention for managing depression and anxiety? A literature review.Explore202319566968010.1016/j.explore.2023.02.007 36868988
    [Google Scholar]
  97. WestfallS. CarracciF. EstillM. Optimization of probiotic therapeutics using machine learning in an artificial human gastrointestinal tract.Sci. Rep.2021111106710.1038/s41598‑020‑79947‑y 33441743
    [Google Scholar]
  98. QureshiR IrfanM AliH Artificial intelligence and biosensors in healthcare and its clinical relevance: A review.IEEE Access202311616002010.1109/ACCESS.2023.3285596
    [Google Scholar]
  99. KumarS. Role of artificial intelligence/machine learning in drug discovery for reducing launch timelines.202310.13140/RG.2.2.23352.32006
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