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2000
Volume 25, Issue 16
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

The field of drug design has evolved from conventional approaches relying on empirical evidence to advanced approaches such as Computer-Aided Drug Design (CADD). It aids in intricate phases of drug discovery, such as target discovery, lead optimization, and clinical trials, establishing a safe, rapid, and cost-effective system. Structure based drug design (SBDD), Ligand based drug design (LBDD), and Pharmacophore modelling, being the most utilized techniques of CADD, play a major role in establishing the road map necessary for the discovery. Artificial intelligence (AI) and Machine learning (ML) have improved the field with the incorporation of big data and, thereby, enhancing the efficacy and accuracy of the CADD. Deep Learning (DL), a part of AI helps in processing complex and non-linear data and thereby decreases complexity, increases resource utilization and enhances drug-target interaction prediction. These approaches have revolutionized healthcare by enhancing diagnostic precision and predicting the behavior of drugs. Currently, AI/ML approach has become crucial for rapidly discovering novel insights and transforming healthcare areas lie diagnostics, clinical research, and critical care. In the case of the drug development area, techniques like PBPK modeling and advanced nano-QSAR enhance drug behavior understanding and predict nano material toxicity if any, leading to safe and effective therapeutic predictions and interventions. The advancement of AI/ML techniques will bring accuracy, efficacy, and more patient-tailored responses to the drug development field.

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References

  1. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery.Mol. Divers.2021251315136010.1007/s11030‑021‑10217‑3
     [Google Scholar]
  2. LipinskiC.F. MaltarolloV.G. OliveiraP.R. da SilvaA.B.F. HonorioK.M. Advances and perspectives in applying deep learning for drug design and discovery.Front. Robot. AI2019610810.3389/frobt.2019.00108 33501123
     [Google Scholar]
  3. 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]
  4. VemulaD. JayasuryaP. SushmithaV. KumarY.N. BhandariV. CADD, AI and ML in drug discovery: A comprehensive review.Eur. J. Pharm. Sci.202318110632410.1016/j.ejps.2022.106324
     [Google Scholar]
  5. ZhangL. TanJ. HanD. ZhuH. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery.Drug Discov. Today201722111680168510.1016/j.drudis.2017.08.010 28881183
     [Google Scholar]
  6. PathakD.V. VyasA. SagarS.R. BhattH.G. PatelP.K. Pharmacophore Mapping: An Important Tool in Modern Drug Design and Discovery.Appl Comput Drug Des Model Methods202357
     [Google Scholar]
  7. KubatM. An Introduction to Machine Learning.Springer201710.1007/978‑3‑319‑63913‑0
     [Google Scholar]
  8. SchmidhuberJ. Deep learning in neural networks: An overview.Neural Netw.2015618511710.1016/j.neunet.2014.09.003 25462637
     [Google Scholar]
  9. LeidnerF. Kurt YilmazN. SchifferC.A. Target-specific prediction of ligand affinity with structure-based interaction fingerprints.J. Chem. Inf. Model.20195993679369110.1021/acs.jcim.9b00457 31381335
     [Google Scholar]
  10. LiZ. HanP. YouZ.H. LiX. ZhangY. YuH. NieR. ChenX. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences.Sci. Rep.2017711117410.1038/s41598‑017‑10724‑0 28894115
     [Google Scholar]
  11. ZernovV.V. BalakinK.V. IvaschenkoA.A. SavchukN.P. PletnevI.V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.J. Chem. Inf. Comput. Sci.20034362048205610.1021/ci0340916 14632457
     [Google Scholar]
  12. KeijsersNLW Neural NetworksEncyclopedia of Movement DisordersAcademic Press2010257259
     [Google Scholar]
  13. ChandershekarA. A review on computer aided drug design (CAAD) and it’s implications in drug discovery and development process.IJHBS2020112733
     [Google Scholar]
  14. MurrayC.W. NewellD.R. AngibaudP. A successful collaboration between academia, biotech and pharma led to discovery of erdafitinib, a selective FGFR inhibitor recently approved by the FDA.MedChemComm20191091509151110.1039/C9MD90044F
     [Google Scholar]
  15. BouribabA. ErrouguiA. ChtitaS. CADD methods for developing novel compounds synthesized to inhibit tyrosine kinase receptors.Curr. Top. Med. Chem.20242410.2174/0115680266312422240712053821 39113295
     [Google Scholar]
  16. DaraS. DhamercherlaS. JadavS.S. BabuC.M. AhsanM.J. Machine Learning in drug discovery: A review.Artif. Intell. Rev.20225531947199910.1007/s10462‑021‑10058‑4
     [Google Scholar]
  17. Prieto-MartínezF.D. López-LópezE. Eurídice Juárez-MercadoK. Medina-FrancoJ.L. Computational drug design methods—current and future perspectives.In Silico Drug DesignAcademic Press20193194410.1016/B978‑0‑12‑816125‑8.00002‑X
     [Google Scholar]
  18. ChoudhuryC. Arul MuruganN. PriyakumarU.D. Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.Drug Discov. Today20222771847186110.1016/j.drudis.2022.03.006 35301148
     [Google Scholar]
  19. StephensonN. ShaneE. ChaseJ. RowlandJ. RiesD. JusticeN. ZhangJ. ChanL. CaoR. Survey of Machine Learning techniques in drug discovery.Curr. Drug Metab.201920318519310.2174/1389200219666180820112457 30124147
     [Google Scholar]
  20. ZhangC. BengioS. HardtM. RechtB. VinyalsO. Understanding deep learning (still) requires rethinking generalization.Commun. ACM202164310711510.1145/3446776
     [Google Scholar]
  21. LavecchiaA. Deep learning in drug discovery: Opportunities, challenges and future prospects.Drug Discov. Today201924102017203210.1016/j.drudis.2019.07.006 31377227
     [Google Scholar]
  22. 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]
  23. WangL. WuY. DengY. KimB. PierceL. KrilovG. LupyanD. RobinsonS. DahlgrenM.K. GreenwoodJ. RomeroD.L. MasseC. KnightJ.L. SteinbrecherT. BeumingT. DammW. HarderE. ShermanW. BrewerM. WesterR. MurckoM. FryeL. FaridR. LinT. MobleyD.L. JorgensenW.L. BerneB.J. FriesnerR.A. AbelR. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.J. Am. Chem. Soc.201513772695270310.1021/ja512751q 25625324
     [Google Scholar]
  24. JohnsonD.K. KaranicolasJ. Ultra-high-throughput structure-based virtual screening for small-molecule inhibitors of protein-protein interactions.J. Chem. Inf. Model.201656239941110.1021/acs.jcim.5b00572 26726827
     [Google Scholar]
  25. EkinsS. PuhlA.C. ZornK.M. LaneT.R. RussoD.P. KleinJ.J. HickeyA.J. ClarkA.M. Exploiting machine learning for end-to-end drug discovery and development.Nat. Mater.201918543544110.1038/s41563‑019‑0338‑z 31000803
     [Google Scholar]
  26. MayrA. KlambauerG. UnterthinerT. HochreiterS. DeepTox: Toxicity prediction using deep learning.Front. Environ. Sci.20163FEB
     [Google Scholar]
  27. XuY. DaiZ. ChenF. GaoS. PeiJ. LaiL. Deep Learning for drug-induced liver injury.J. Chem. Inf. Model.201555102085209310.1021/acs.jcim.5b00238 26437739
     [Google Scholar]
  28. SeglerM.H.S. PreussM. WallerM.P. Planning chemical syntheses with deep neural networks and symbolic AI.Nature2018555769860461010.1038/nature25978 29595767
     [Google Scholar]
  29. DudleyJ.T. DeshpandeT. ButteA.J. Exploiting drug-disease relationships for computational drug repositioning.Brief. Bioinform.201112430331110.1093/bib/bbr013 21690101
     [Google Scholar]
  30. RamsundarB. LiuB. WuZ. VerrasA. TudorM. SheridanR.P. PandeV. Is multitask deep learning practical for pharma?J. Chem. Inf. Model.20175782068207610.1021/acs.jcim.7b00146 28692267
     [Google Scholar]
  31. CamachoD.M. CollinsK.M. PowersR.K. CostelloJ.C. CollinsJ.J. Next-generation Machine Learning for biological networks.Cell201817371581159210.1016/j.cell.2018.05.015 29887378
     [Google Scholar]
  32. VamathevanJ. ClarkD. CzodrowskiP. DunhamI. FerranE. LeeG. LiB. MadabhushiA. ShahP. SpitzerM. ZhaoS. Applications of machine learning in drug discovery and development.Nat. Rev. Drug Discov.201918646347710.1038/s41573‑019‑0024‑5 30976107
     [Google Scholar]
  33. NiaziS. The coming of age of AI/ML in drug discovery, development, clinical testing, and manufacturing: The FDA perspectives.Drug Des. Devel. Ther.2023172691272510.2147/DDDT.S424991 37701048
     [Google Scholar]
  34. TopolE.J. High-performance medicine: The convergence of human and artificial intelligence.Nat. Med.2019251445610.1038/s41591‑018‑0300‑7 30617339
     [Google Scholar]
  35. BycroftC. FreemanC. PetkovaD. BandG. ElliottL.T. SharpK. MotyerA. VukcevicD. DelaneauO. O’ConnellJ. CortesA. WelshS. YoungA. EffinghamM. McVeanG. LeslieS. AllenN. DonnellyP. MarchiniJ. The UK Biobank resource with deep phenotyping and genomic data.Nature2018562772620320910.1038/s41586‑018‑0579‑z 30305743
     [Google Scholar]
  36. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
     [Google Scholar]
  37. ŠaliA. BlundellT.L. Comparative protein modelling by satisfaction of spatial restraints.J. Mol. Biol.1993234377981510.1006/jmbi.1993.1626 8254673
     [Google Scholar]
  38. RoseG.D. GeselowitzA.R. LesserG.J. LeeR.H. ZehfusM.H. Hydrophobicity of amino acid residues in globular proteins.Science19852294716834838
     [Google Scholar]
  39. SinghP. SharmaP. BisettyK. PerezJ.J. Molecular dynamics simulations of Ac-3Aib-Cage-3Aib-NHMe.Mol. Simul.201036131035104410.1080/08927022.2010.501797
     [Google Scholar]
  40. JonesD.T. TaylortW.R. ThorntonJ.M. A new approach to protein fold recognition.Nature19923586381868910.1038/358086a0 1614539
     [Google Scholar]
  41. SimonsK.T. KooperbergC. HuangE. BakerD. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions.J. Mol. Biol.1997268120922510.1006/jmbi.1997.0959 9149153
     [Google Scholar]
  42. WallachI. HeifetsA. Most ligand-based classification benchmarks reward memorization rather than generalization.J. Chem. Inf. Model.201858591693210.1021/acs.jcim.7b00403 29698607
     [Google Scholar]
  43. ShawD.E. DeneroffM.M. DrorR.O. KuskinJ.S. LarsonR.H. SalmonJ.K. YoungC. BatsonB. BowersK.J. ChaoJ.C. EastwoodM.P. GagliardoJ. GrossmanJ.P. HoC.R. IerardiD.J. KolossváryI. KlepeisJ.L. LaymanT. McLeaveyC. MoraesM.A. MuellerR. PriestE.C. ShanY. SpenglerJ. TheobaldM. TowlesB. WangS.C. Anton, a special-purpose machine for molecular dynamics simulation.Proc. Int. Symp. Comput. Archit.200711210.1145/1250662.1250664
     [Google Scholar]
  44. RibeiroJ.M.L. BravoP. WangY. TiwaryP. Reweighted autoencoded variational Bayes for enhanced sampling (RAVE).J. Chem. Phys.2018149707230110.1063/1.5025487 30134694
     [Google Scholar]
  45. HeidariM. KremerK. GolestanianR. PotestioR. Cortes-HuertoR. Open-boundary Hamiltonian adaptive resolution. From grand canonical to non-equilibrium molecular dynamics simulations.J. Chem. Phys.20201521919410410.1063/1.5143268 33687261
     [Google Scholar]
  46. ReedJ.E. SmaillJ.B. The discovery of Dacomitinib, a potent irreversible EGFR inhibitorACS Symposium Series2016Volume 1207233SE10.1021/bk‑2016‑1239.ch008
     [Google Scholar]
  47. RutenberE.E. StroudR.M. Binding of the anticancer drug ZD1694 to E. coli thymidylate synthase: Assessing specificity and affinity.Structure19964111317132410.1016/S0969‑2126(96)00139‑6 8939755
     [Google Scholar]
  48. CuiJ.J. Tran-DubéM. ShenH. NambuM. KungP.P. PairishM. JiaL. MengJ. FunkL. BotrousI. McTigueM. GrodskyN. RyanK. PadriqueE. AltonG. TimofeevskiS. YamazakiS. LiQ. ZouH. ChristensenJ. MroczkowskiB. BenderS. KaniaR.S. EdwardsM.P. Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK).J. Med. Chem.201154186342636310.1021/jm2007613 21812414
     [Google Scholar]
  49. BarkerA. AndrewsD. Discovery and development of the anticancer agent gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase.Introduction to Biological and Small Molecule Drug Research and Development.Elsevier2013255281
     [Google Scholar]
  50. SchindlerC.G. ArmbrustT. GunawanB. LangerC. FüzesiL. RamadoriG. Gastrointestinal stromal tumor (GIST) -- Single center experience of prolonged treatment with imatinib.Z. Gastroenterol.200543326727310.1055/s‑2004‑813756 15765299
     [Google Scholar]
  51. HengD.Y.C. Kollmannsberger, C. Sunitinib.Recent Results Cancer Res.20097182
     [Google Scholar]
  52. CurtinN.J. The development of Rucaparib/Rubraca®: A story of the synergy between science and serendipity.Cancers202012356410.3390/cancers12030564 32121331
     [Google Scholar]
  53. HarperS. McCauleyJ.A. RuddM.T. FerraraM. DiFilippoM. CrescenziB. KochU. PetrocchiA. HollowayM.K. ButcherJ.W. RomanoJ.J. BushK.J. GilbertK.F. McIntyreC.J. NguyenK.T. NiziE. CarrollS.S. LudmererS.W. BurleinC. DiMuzioJ.M. GrahamD.J. McHaleC.M. StahlhutM.W. OlsenD.B. MonteagudoE. CianettiS. GiulianoC. PucciV. TrainorN. FandozziC.M. RowleyM. ColemanP.J. VaccaJ.P. SummaV. LivertonN.J. Discovery of MK-5172, a macrocyclic hepatitis C virus NS3/4a protease inhibitor.ACS Med. Chem. Lett.20123433233610.1021/ml300017p 24900473
     [Google Scholar]
  54. RaoB.G. MurckoM. TebbeM.J. KwongA.D. Discovery and development of Telaprevir (IncivekTM)-a protease inhibitor to treat hepatitis C infection.Success Drug Discov2015195212
     [Google Scholar]
  55. GhoshA.K. DawsonZ.L. MitsuyaH. Darunavir, a conceptually new HIV-1 protease inhibitor for the treatment of drug-resistant HIV.Bioorg. Med. Chem.200715247576758010.1016/j.bmc.2007.09.010 17900913
     [Google Scholar]
  56. VeljkovicN. VucicevicJ. TassiniS. GlisicS. VeljkovicV. RadiM. Preclinical discovery and development of maraviroc for the treatment of HIV.Expert Opin. Drug Discov.201510667168410.1517/17460441.2015.1041497 25927601
     [Google Scholar]
  57. BestB.M. GoicoecheaM. Efavirenz--still first-line king?Expert Opin. Drug Metab. Toxicol.20084796597210.1517/17425255.4.7.965 18624683
     [Google Scholar]
  58. AdamsW.J. AristoffP.A. JensenR.K. MorozowichW. RomeroD.L. SchinzerW.C. Discovery and Development of the BHAP Nonnucleoside Reverse Transcriptase Inhibitor Delavirdine Mesylate BT - Integration of Pharmaceutical Discovery and Development: Case Histories; Borchardt, R.T.; Freidinger, R.M.; Sawyer, T.K. SmithP.L. Boston, MASpringer199828531210.1007/0‑306‑47384‑4_13
     [Google Scholar]
  59. AruksakunwongO. PromsriS. WittayanarakulK. NimmanpipugP. LeeV. WijitkosoomA. SompornpisutP. HannongbuaS. Current development on HIV-1 protease inhibitors.Curr. Computeraided Drug Des.20073320121310.2174/157340907781695431
     [Google Scholar]
  60. MarkowitzM. SaagM. PowderlyW.G. HurleyA.M. HsuA. ValdesJ.M. HenryD. SattlerF. MarcaA.L. LeonardJ.M. HoD.D. A preliminary study of ritonavir, an inhibitor of HIV-1 protease, to treat HIV-1 infection.N. Engl. J. Med.1995333231534154010.1056/NEJM199512073332204 7477168
     [Google Scholar]
  61. WlodawerA. VondrasekJ. Inhibitors of HIV-1 protease: A major success of structure-assisted drug design.Annu. Rev. Biophys. Biomol. Struct.199827124928410.1146/annurev.biophys.27.1.249 9646869
     [Google Scholar]
  62. LewW. ChenX. KimC.U. Discovery and development of GS 4104 (oseltamivir): An orally active influenza neuraminidase inhibitor.Curr. Med. Chem.20007666367210.2174/0929867003374886 10702632
     [Google Scholar]
  63. De ClercqE. Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV.Int. J. Antimicrob. Agents200933430732010.1016/j.ijantimicag.2008.10.010 19108994
     [Google Scholar]
  64. LyleT.A. Ribonucleic acid viruses: Antivirals for human immunodeficiency virus.Compr Med Chem2007II32937110.1016/B0‑08‑045044‑X/00213‑3
     [Google Scholar]
  65. PerzbornE. RoehrigS. StraubA. KubitzaD. MisselwitzF. The discovery and development of rivaroxaban, an oral, direct factor Xa inhibitor.Nat. Rev. Drug Discov.2011101617510.1038/nrd3185 21164526
     [Google Scholar]
  66. AulakhG.K. SodhiR.K. SinghM. An update on non-peptide angiotensin receptor antagonists and related RAAS modulators.Life Sci.200781861563910.1016/j.lfs.2007.06.007 17692338
     [Google Scholar]
  67. Claude CohenN. Structure-based drug design and the discovery of aliskiren (Tekturna): Perseverance and creativity to overcome a R&D pipeline challenge.Chem. Biol. Drug Des.200770655756510.1111/j.1747‑0285.2007.00599.x 17999663
     [Google Scholar]
  68. GoaK.L. NobleS. Eptifibatide.Drugs199957343946210.2165/00003495‑199957030‑00015 10193692
     [Google Scholar]
  69. CookJ.J. BednarB. LynchJ.J.Jr GouldR.J. EgbertsonM.S. HalczenkoW. DugganM.E. HartmanG.D. LoM-W. MurphyG.M. DeckelbaumL.I. SaxF.L. BarrE. Tirofiban (Aggrastat®).Cardiovasc. Drug Rev.199917319922410.1111/j.1527‑3466.1999.tb00015.x
     [Google Scholar]
  70. CushmanD.W. OndettiM.A. History of the design of captopril and related inhibitors of angiotensin converting enzyme.Hypertension199117458959210.1161/01.HYP.17.4.589 2013486
     [Google Scholar]
  71. AbidiA. ShuklaP. AhmadA. Lifitegrast: A novel drug for treatment of dry eye disease.J. Pharmacol. Pharmacother.20167419419810.4103/0976‑500X.195920 28163544
     [Google Scholar]
  72. FischerJános Analogue-based drug discovery.Chem. Int.2010324121510.1515/ci.2010.32.4.12
     [Google Scholar]
  73. WarnkeC. WiendlH. HartungH.P. StüveO. KieseierB.C. Identification of targets and new developments in the treatment of multiple sclerosis--focus on cladribine.Drug Des. Devel. Ther.20104117126 20689698
     [Google Scholar]
  74. BookserB.C. ReddyK.R. BoyerS.H. HeckerS.J. Vaborbactam (in Combination with Meropenem as Vabomere), a non-β-lactam β-Lactamase inhibitor for treatment of complicated Urinary Tract infections and Pyelonephritis.Current Drug Synthesis2022174010.1002/9781119847281.ch2
     [Google Scholar]
  75. Avendaño-ValenciaL.D. FassoisS.D. Natural vibration response based damage detection for an operating wind turbine via Random Coefficient Linear Parameter Varying AR modelling.J. Phys. Conf. Ser.2015628101207310.1088/1742‑6596/628/1/012073
     [Google Scholar]
  76. MichaudF. LamasM. LugrísU. CuadradoJ. A fair and EMG-validated comparison of recruitment criteria, musculotendon models and muscle coordination strategies, for the inverse-dynamics based optimization of muscle forces during gait.J. Neuroeng. Rehabil.20211811710.1186/s12984‑021‑00806‑6 33509205
     [Google Scholar]
  77. OngC.S. ScientificT.C. SmolaA.J. Protein function prediction via graph kernels.Bioinformatics200521Suppl. 1i47i56
     [Google Scholar]
  78. HantzE.R. LindertS. Applications of machine learning in computer-aided drug discovery.QRB Discovery20223e14
     [Google Scholar]
  79. BreimanLEO Random forests.ML200145532
     [Google Scholar]
  80. ChenB. HarrisonR.F. PapadatosG. WillettP. WoodD.J. LewellX.Q. GreenidgeP. StieflN. Evaluation of machine-learning methods for ligand-based virtual screening.J. Comput. Aided Mol. Des.2007211-3536210.1007/s10822‑006‑9096‑5 17205373
     [Google Scholar]
  81. A probabilistic perspective.Chance Encounters: Probability in EducationSpringer: Dordrecht2012122771
     [Google Scholar]
  82. KirasichK. Random forest vs logistic regression: Binary classification for heterogeneous datasets.SMU Data Sci Rev2018139
     [Google Scholar]
  83. HouT. BianY. McGuireT. XieX.Q. Integrated multi-class classification and prediction of gpcr allosteric modulators by machine learning intelligence.Biomolecules202111687010.3390/biom11060870 34208096
     [Google Scholar]
  84. HayanoM. NogawaK. KidoT. KobayashiE. HondaR. TuritaniI. Dose-response relationship between urinary cadmium concentration and β2-microglobulinuria using logistic regression analysis.Arch. Environ. Health199651216216710.1080/00039896.1996.9936011 8638969
     [Google Scholar]
  85. TaborelliM. MontellaM. LibraM. TedeschiR. CrispoA. GrimaldiM. Dal MasoL. SerrainoD. PoleselJ. The dose-response relationship between tobacco smoking and the risk of lymphomas: A case-control study.BMC Cancer201717142110.1186/s12885‑017‑3414‑2 28622762
     [Google Scholar]
  86. LiemY. JudgeA. KirwanJ. OurradiK. LiY. SharifM. Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritis.Sci. Rep.20201011132810.1038/s41598‑020‑68077‑0 32647218
     [Google Scholar]
  87. Applied Regression AnalysisA Research Tool.New YorkSpringer1998
     [Google Scholar]
  88. GombarV.K. HallS.D. Quantitative structure-activity relationship models of clinical pharmacokinetics: Clearance and volume of distribution.J. Chem. Inf. Model.201353494895710.1021/ci400001u 23451981
     [Google Scholar]
  89. ShahlaeiM. Madadkar-SobhaniA. FassihiA. SaghaieL. ShamshirianD. SakhiH. Comparative quantitative structure-activity relationship study of some 1-aminocyclopentyl-3-carboxyamides as CCR2 inhibitors using stepwise MLR, FA-MLR, and GA-PLS.Med. Chem. Res.201221110011510.1007/s00044‑010‑9501‑4
     [Google Scholar]
  90. LinM. FanB. LuiJ.C.S. ChiuD.M. Stochastic analysis of file-swarming systems.PE2007649-1285687510.1016/j.peva.2007.06.006
     [Google Scholar]
  91. BalakinK.V. Ekins, S Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery.HobokenJohn Wiley & Sons2009584
     [Google Scholar]
  92. VanMenC. JangY.S. ZhuH.M. LeeJ.H. TrungT.N. NgocT.M. KimY.H. KangJ.S. Chemical-based species classification of rhubarb using simultaneous determination of five bioactive substances by HPLC and LDA analysis.Phytochem. Anal.201223435936410.1002/pca.1365 22009582
     [Google Scholar]
  93. PérezM.A.C. SanzM.B. TorresL.R. ÁvalosR.G. GonzálezM.P. DíazH.G. A topological sub-structural approach for predicting human intestinal absorption of drugs.Eur. J. Med. Chem.2004391190591610.1016/j.ejmech.2004.06.012 15501539
     [Google Scholar]
  94. ShafferR.E. Rose-pehrssonS.L. Improved probabilistic neural network algorithm for chemical sensor array pattern recognition.Anal. Chem.1999711942634271
     [Google Scholar]
  95. MehrotraA. SinghK.K. NigamM.J. PalK. Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network.Nat. Hazards201577136738110.1007/s11069‑015‑1595‑z
     [Google Scholar]
  96. NiwaT. Prediction of biological targets using probabilistic neural networks and atom-type descriptors.J. Med. Chem.200447102645265010.1021/jm0302795 15115405
     [Google Scholar]
  97. RaposoL.M. ArrudaM.B. de BrindeiroR.M. NobreF.F. Lopinavir resistance classification with imbalanced data using probabilistic neural networks.J. Med. Syst.20164036910.1007/s10916‑015‑0428‑7 26733278
     [Google Scholar]
  98. GoodfellowI. Deep LearningMIT Press.20175217553785
     [Google Scholar]
  99. TripathiM.K. NathA. SinghT.P. EthayathullaA.S. KaurP. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Mol. Divers.20212531439146010.1007/s11030‑021‑10256‑w 34159484
     [Google Scholar]
  100. SerranoD.R. LucianoF.C. AnayaB.J. OngorenB. KaraA. MolinaG. RamirezB.I. Sánchez-GuiralesS.A. SimonJ.A. TomiettoG. RaptiC. RuizH.K. RawatS. KumarD. LalatsaA. Artificial Intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine.Pharmaceutics20241610132810.3390/pharmaceutics16101328 39458657
     [Google Scholar]
  101. VisanA.I. NegutI. Integrating Artificial Intelligence for drug discovery in the context of revolutionizing drug delivery.Life202414223310.3390/life14020233 38398742
     [Google Scholar]
  102. ParkH. YamaguchiR. ImotoS. MiyanoS. Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.PLoS One2022175e026163010.1371/journal.pone.0261630 35584089
     [Google Scholar]
  103. AbouirK. SamerC.F. GloorY. DesmeulesJ.A. DaaliY. Reviewing data integrated for PBPK model development to predict metabolic drug-drug interactions: Shifting perspectives and emerging trends.Front. Pharmacol.20211270829910.3389/fphar.2021.708299 34776945
     [Google Scholar]
  104. HabiballahS. ReisfeldB. Adapting physiologically-based pharmacokinetic models for machine learning applications.Sci. Rep.20231311493410.1038/s41598‑023‑42165‑3 37696914
     [Google Scholar]
  105. SinghA.V. AnsariM.H.D. RosenkranzD. MaharjanR.S. KriegelF.L. GandhiK. KanaseA. SinghR. LauxP. LuchA. Artificial Intelligence and Machine Learning in computational nanotoxicology: Unlocking and empowering nanomedicine.Adv. Healthc. Mater.2020917190186210.1002/adhm.201901862 32627972
     [Google Scholar]
  106. LiJ. WangC. YueL. ChenF. CaoX. WangZ. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review.Ecotoxicol. Environ. Saf.202224311395510.1016/j.ecoenv.2022.113955 35961199
     [Google Scholar]
  107. ZhangX. LionbergerR.A. DavitB.M. YuL.X. Utility of physiologically based absorption modeling in implementing Quality by Design in drug development.AAPS J.2011131597110.1208/s12248‑010‑9250‑9 21207216
     [Google Scholar]
  108. ZhangF. WangZ. PeijnenburgW.J.G.M. VijverM.G. Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles.Environ. Int.202317710802510.1016/j.envint.2023.108025 37329761
     [Google Scholar]
  109. LauxP. RiebelingC. BoothA.M. BrainJ.D. BrunnerJ. CerrilloC. Challenges in characterizing the environmental fate and effects of carbon nanotubes and inorganic nanomaterials in aquatic systems.Environ. Sci. Nano201854863
     [Google Scholar]
  110. KhetrapalN. Cognition meets assistive technology: Insights from load theory of selective attention.Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives.IGI Global20109610810.4018/978‑1‑61520‑817‑3.ch006
     [Google Scholar]
  111. IwataH. Application of in silico technologies for drug target discovery and pharmacokinetic analysis.Chem. Pharm. Bull.202371639840510.1248/cpb.c22‑00638 37258192
     [Google Scholar]
/content/journals/ctmc/10.2174/0115680266346722250401191232
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The Future of Medicine: AI and ML Driven Drug Discovery Advancements
Curr. Top. Med. Chem. 25, 1957 (2025); https://doi.org/10.2174/0115680266346722250401191232
/content/journals/ctmc/10.2174/0115680266346722250401191232
/content/journals/ctmc/10.2174/0115680266346722250401191232
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  • Article Type:     Review Article
Keyword(s): artificial intelligence; artificial neural network; Computer-aided drug design; convolutional neural network; machine learning

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