Skip to content
2000
Volume 2, Issue 1
  • ISSN: 2950-3752
  • E-ISSN: 2950-3760

Abstract

Computer-aided drug design has revolutionized the landscape of drug discovery, accompanied by a new era of innovation and efficiency of novel therapeutic agents. This article review explores the diverse innovations and practical applications that have propelled CADD into the forefront of modern medicine. CADD, a multidisciplinary field at the interaction of biology, chemistry, and computational science, offers a toolkit for the identification and development of pharmaceutical compounds. It has the ability to predict molecular interaction between drug and biological targets with remarkable precision, reducing the dependency on laborious and costly laboratory experiments. The review deals with two primary domains of CADD: structure-based and ligand-based design. This three-dimensional protein structure and screening of chemical libraries have led to rational changes. The analysis of known drug compounds' chemical and biological properties has enabled the creation of predictive models, opening new routes for drug discovery. The impact of CADD on the pharmaceutical industry is clear. This review highlights its instrumental role in the development of antiviral agents, cancer therapeutics, and treatment for various diseases. The transformation potential of CADD is not without challenges, including the need for substantial computational resources and the essential requirement of experimental analysis. The synergy between innovation and practical application is clear, driving unexpected efficiency and precision in the identification of therapeutic solutions. As pharmaceutical research continues to evolve, the role of CADD remains pivotal, assuring the rapid translation of scientific innovation into real-world medical advancements.

Loading

Article metrics loading...

/content/journals/cai/10.2174/0129503752321279241126091807
2024-12-16
2025-09-28
Loading full text...

Full text loading...

References

  1. MarshallG.R. Computer-aided drug design.Annu. Rev. Pharmacol. Toxicol.198727119321310.1146/annurev.pa.27.040187.001205 3555315
    [Google Scholar]
  2. SurabhiS. SinghB.K. Computer aided drug design: An overview.J. Drug Deliv. Ther.20188550450910.22270/jddt.v8i5.1894
    [Google Scholar]
  3. VeselovskyA. IvanovA. Strategy of computer-aided drug design.Curr. Drug Targets Infect. Disord.200331334010.2174/1568005033342145 12570731
    [Google Scholar]
  4. GünerO. History and evolution of the pharmacophore concept in computer-aided drug design.Curr. Top. Med. Chem.20022121321133210.2174/1568026023392940 12470283
    [Google Scholar]
  5. TaftC.A. da SilvaV.B. da SilvaC.H.T.P. Current topics in computer‐aided drug design.J. Pharm. Sci.20089731089109810.1002/jps.21293 18214973
    [Google Scholar]
  6. BaigM.H. AhmadK. RabbaniG. DanishuddinM. ChoiI. Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders.Curr. Neuropharmacol.201816674074810.2174/1570159X15666171016163510 29046156
    [Google Scholar]
  7. MezeyP.G. Computer aided drug design: Some fundamental aspects.Mol Mod Ann2000615015710.1007/PL00010725
    [Google Scholar]
  8. ZhangS. Computer-aided drug discovery and development.Drug Design and Discovery. SatyanarayanajoisS. Humana Press2011233810.1007/978‑1‑61779‑012‑6_2
    [Google Scholar]
  9. YuW. MacKerellA.D. Computer-aided drug design methods.Antibiotics. SassP. New York, NYHumana Press20178510610.1007/978‑1‑4939‑6634‑9_5
    [Google Scholar]
  10. BassaniD. MoroS. Past, present, and future perspectives on computer-aided drug design methodologies.Molecules2023289390610.3390/molecules28093906 37175316
    [Google Scholar]
  11. MacalinoS.J.Y. GosuV. HongS. ChoiS. Role of computer-aided drug design in modern drug discovery.Arch. Pharm. Res.20153891686170110.1007/s12272‑015‑0640‑5 26208641
    [Google Scholar]
  12. IwaloyeO. OttuP.O. OlawaleF. Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward?Inform Med Unlocked20234110133210.1016/j.imu.2023.101332
    [Google Scholar]
  13. Van DrieJ.H. Computer-aided drug design: The next 20 years.J. Comput. Aided Mol. Des.20072110-1159160110.1007/s10822‑007‑9142‑y 17989929
    [Google Scholar]
  14. LiontaE. SpyrouG. VassilatisD. CourniaZ. Structure-based virtual screening for drug discovery: Principles, applications and recent advances.Curr. Top. Med. Chem.201414161923193810.2174/1568026614666140929124445 25262799
    [Google Scholar]
  15. PliushcheuskayaP. KünzeG. Recent advances in computer-aided structure-based drug design on ion channels.Int. J. Mol. Sci.20232411922610.3390/ijms24119226 37298178
    [Google Scholar]
  16. NascimentoI.J.S. de AquinoT.M. da Silva-JúniorE.F. The new era of drug discovery: The power of computer-aided drug design (CADD).Lett. Drug Des. Discov.2022191195195510.2174/1570180819666220405225817
    [Google Scholar]
  17. TabeshpourJ. SahebkarA. ZirakM.R. Computer-aided drug design and drug pharmacokinetic prediction: A mini-review.Curr. Pharm. Des.201824263014301910.2174/1381612824666180903123423 30179125
    [Google Scholar]
  18. DouguetD. Munier-LehmannH. LabesseG. PochetS. LEA3D: A computer-aided ligand design for structure-based drug design.J. Med. Chem.20054872457246810.1021/jm0492296 15801836
    [Google Scholar]
  19. HawkinsP.C. StahlG. Ligand-based methods in GPCR computer-aided drug design.Computational Methods for GPCR Drug Discovery. HeifetzA. New York, NYHumana Press201836537410.1007/978‑1‑4939‑7465‑8_18
    [Google Scholar]
  20. YeJ. YangX. MaC. Ligand-based drug design of novel antimicrobials against Staphylococcus aureus by targeting bacterial transcription.Int. J. Mol. Sci.202224133910.3390/ijms24010339 36613782
    [Google Scholar]
  21. DainaA. BlatterM.C. Baillie GerritsenV. Drug design workshop: A web-based educational tool to introduce computer-aided drug design to the general public.J. Chem. Educ.201794333534410.1021/acs.jchemed.6b00596
    [Google Scholar]
  22. KrügerD.M. EversA. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors.ChemMedChem20105114815810.1002/cmdc.200900314 19908272
    [Google Scholar]
  23. HallD.R. EnyedyI.J. The use of fake ligands from computational solvent mapping in ligand and structure-based virtual screening.Future Med. Chem.20168151815182310.4155/fmc‑2016‑0115 27630057
    [Google Scholar]
  24. ArunachalamM. ChiranjeevC. MondalB. SanjayT. Generative AI revolution: Shaping the future of healthcare innovation.Revolutionizing the Healthcare Sector with AI.IGI Global202434136410.4018/979‑8‑3693‑3731‑8.ch017
    [Google Scholar]
  25. 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 36347444
    [Google Scholar]
  26. GuR. WuF. HuangZ. Role of computer-aided drug design in drug development.Molecules20232820716010.3390/molecules28207160 37894639
    [Google Scholar]
  27. SpiveyR.N. JonesJ.K. WardellW. VodraW.W. The US FDA in the drug development, evaluation and approval process. In: Griffin JP, Posner J, Barker GR, Eds. The Textbook of Pharmaceutical Medicine. Wiley201310.1002/9781118532331.ch25
    [Google Scholar]
  28. Scott DixonJ. Computer-aided drug design: Getting the best results.Trends Biotechnol.1992101035736310.1016/0167‑7799(92)90268‑Z 1368875
    [Google Scholar]
  29. KhanM.F. KandwalS. FayneD. DataPype: A fully automated unified software platform for computer-aided drug design.ACS Omega2023842394683948010.1021/acsomega.3c05207 37901539
    [Google Scholar]
  30. LloydD.G. GolfisG. KnoxA.J.S. FayneD. MeeganM.J. OpreaT.I. Oncology exploration: Charting cancer medicinal chemistry space.Drug Discov. Today2006113-414915910.1016/S1359‑6446(05)03688‑3 16533713
    [Google Scholar]
  31. MoretM. Pachon AngonaI. CotosL. Leveraging molecular structure and bioactivity with chemical language models for] de novo drug design.Nat. Commun.202314111410.1038/s41467‑022‑35692‑6 36611029
    [Google Scholar]
  32. PinziL. RastelliG. Molecular docking: Shifting paradigms in drug discovery.Int. J. Mol. Sci.20192018433110.3390/ijms20184331 31487867
    [Google Scholar]
  33. OomsF. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry.Curr. Med. Chem.20007214115810.2174/0929867003375317 10637360
    [Google Scholar]
  34. AcunaV.V. HopperR.M. YoderR.J. Computer-aided drug design for the organic chemistry laboratory using accessible molecular modeling tools.J. Chem. Educ.202097376076310.1021/acs.jchemed.9b00592
    [Google Scholar]
  35. AnwarT. KumarP. KhanA.U. Modern tools and techniques in computer-aided drug design.Molecular Docking for Computer-Aided Drug Design.Chapter 1Academic Press202113010.1016/B978‑0‑12‑822312‑3.00011‑4
    [Google Scholar]
  36. AbdolmalekiA. GhasemiJ. GhasemiF. Computer aided drug design for multi-target drug design: SAR/QSAR, molecular docking and pharmacophore methods.Curr. Drug Targets201718555657510.2174/1389450117666160101120822 26721410
    [Google Scholar]
  37. RomanovA.N. Grigor’evF.V. SulimovA.V. LushchekinaS.V. MartynovY.B. SulimovV.B. The SOL docking package for computer-aided drug design.Num Meth Prog200893213233
    [Google Scholar]
  38. EdacheE.I. UzairuA. MamzaP.A. Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.Front. Mol. Biosci.202310125423010.3389/fmolb.2023.1254230 37771457
    [Google Scholar]
  39. WeiZ. High performance computing simulation of intelligent logistics management based on shortest path algorithm.Comput. Intell. Neurosci.20222022111010.1155/2022/7930553 35720907
    [Google Scholar]
  40. SuhagD. KaushikS. TaxakV.B. Challenges and future directions.Handbook of Biomaterials for Medical Applications.SingaporeSpringer2024Vol. 132935510.1007/978‑981‑97‑4818‑1_12
    [Google Scholar]
  41. YangL. SongX. High-performance computing analysis and location selection of logistics distribution center space based on whale optimization algorithm.Comput. Intell. Neurosci.2022202211910.1155/2022/2055241 35785074
    [Google Scholar]
  42. ChristleyS. ScarboroughW. SalinasE. VDJServer: A cloud-based analysis portal and data commons for immune repertoire sequences and rearrangements.Front. Immunol.2018997610.3389/fimmu.2018.00976 29867956
    [Google Scholar]
  43. BhinderB. GilvaryC. MadhukarN.S. ElementoO. Artificial intelligence in cancer research and precision medicine.Cancer Discov.202111490091510.1158/2159‑8290.CD‑21‑0090 33811123
    [Google Scholar]
  44. ZauharR.J. MoynaG. TianL. LiZ. WelshW.J. Shape signatures: A new approach to computer-aided ligand- and receptor-based drug design.J. Med. Chem.200346265674569010.1021/jm030242k 14667221
    [Google Scholar]
  45. 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]
  46. SeibertK. DomhoffD. BruchD. Application scenarios for artificial intelligence in nursing care: Rapid review.J. Med. Internet Res.20212311e2652210.2196/26522 34847057
    [Google Scholar]
  47. DuttaS SutradharS SachanK. Computer-aided drug design - A new approach in drug design and discovery. Computer201043025
  48. 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]
  49. ConnorC.W. Artificial intelligence and machine learning in anesthesiology.Anesthesiology201913161346135910.1097/ALN.0000000000002694 30973516
    [Google Scholar]
  50. SooriM. ArezooB. DastresR. Artificial intelligence, machine learning and deep learning in advanced robotics, a review.Cognitive Robotics20233547010.1016/j.cogr.2023.04.001
    [Google Scholar]
  51. LeeJ.W. Maria-SolanoM.A. VuT.N.L. YoonS. ChoiS. Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).Biochem. Soc. Trans.202250124125210.1042/BST20211240 35076690
    [Google Scholar]
  52. ZhangL LinJ LiuB ZhangZ YanX WeiM. A review on deep learning applications in prognostics and health management.IEEE Access 2019, 7; 162415-3810.1109/ACCESS.2019.2950985
    [Google Scholar]
  53. SubramanianI. VermaS. KumarS. JereA. AnamikaK. Multi-omics data integration, interpretation, and its application.Bioinform. Biol. Insights2020•••1410.1177/1177932219899051 32076369
    [Google Scholar]
  54. ConesaA. BeckS. Making multi-omics data accessible to researchers.Sci. Data20196125110.1038/s41597‑019‑0258‑4 31672978
    [Google Scholar]
  55. NiaziS.K. MariamZ. Computer-aided drug design and drug discovery: A prospective analysis.Pharmaceuticals (Basel)20231712210.3390/ph17010022 38256856
    [Google Scholar]
  56. EjalonibuM.A. OgundareS.A. ElrashedyA.A. Drug discovery for Mycobacterium tuberculosis using structure-based computer-aided drug design approach.Int. J. Mol. Sci.202122241325910.3390/ijms222413259 34948055
    [Google Scholar]
  57. Santiago-RodriguezT.M. HollisterE.B. Multi 'omic data integration: A review of concepts, considerations, and approaches.Semin. Perinatol.202145615145610.1016/j.semperi.2021.151456
    [Google Scholar]
  58. GillS.S. KumarA. SinghH. Quantum computing: A taxonomy, systematic review and future directions.Softw. Pract. Exper.20225216611410.1002/spe.3039
    [Google Scholar]
  59. PreskillJ. Quantum computing 40 years later.Feynman Lectures on Computation.CRC Press202310.1201/9781003358817‑7
    [Google Scholar]
  60. Medina-FrancoJ.L. Grand challenges of computer-aided drug design: The road ahead.Front Drug Discov (Lausanne)2021172855110.3389/fddsv.2021.728551
    [Google Scholar]
  61. ChauhanV. NegiS. JainD. SinghP. SagarA.K. SharmaA.K. Quantum computers: A review on how quantum computing can boom AI.2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)Greater Noida, India, 28-29 Apr202255956310.1109/ICACITE53722.2022.9823619
    [Google Scholar]
  62. EbhohimenI.E. EdemhanriaL. AwojideS. OnyijenO.H. AnywarG. Advances in computer-aided drug discovery.Phytochemicals as Lead Compounds for New Drug Discovery.Chapter 3Elsevier2020253710.1016/B978‑0‑12‑817890‑4.00003‑2
    [Google Scholar]
  63. NarayanA. ChananaA. ShankerO. Role of artificial intelligence in pharmaceutical drug development.Curr Indian Sci202421910.2174/012210299X313252240521111358
    [Google Scholar]
  64. PrachayasittikulV. WorachartcheewanA. ShoombuatongW. Computer-aided drug design of bioactive natural products.Curr. Top. Med. Chem.201515181780180010.2174/1568026615666150506151101 25961523
    [Google Scholar]
  65. KelleyE.W. Computer-aided drug design project for introductory high school students.J. Chem. Educ.202310031179118810.1021/acs.jchemed.2c00989
    [Google Scholar]
  66. KrishnamurthyN. GrimshawA.A. AxsonS.A. ChoeS.H. MillerJ.E. Drug repurposing: A systematic review on root causes, barriers and facilitators.BMC Health Serv. Res.202222197010.1186/s12913‑022‑08272‑z 35906687
    [Google Scholar]
  67. Aguayo-OrtizR. Fernández-de GortariE. Overview of computer-aided drug design for epigenetic targets.Epi-Informatics.Chapter 2Academic Press2016215210.1016/B978‑0‑12‑802808‑7.00002‑2
    [Google Scholar]
  68. DalkasG.A. VlachakisD. TsagkrasoulisD. KastaniaA. KossidaS. State-of-the-art technology in modern computer-aided drug design.Brief. Bioinform.201314674575210.1093/bib/bbs063 23148324
    [Google Scholar]
  69. PushpakomS. IorioF. EyersP.A. Drug repurposing: Progress, challenges and recommendations.Nat. Rev. Drug Discov.2019181415810.1038/nrd.2018.168 30310233
    [Google Scholar]
  70. SemighiniE.P. ResendeJ.A. de AndradeP. Using computer-aided drug design and medicinal chemistry strategies in the fight against diabetes.J. Biomol. Struct. Dyn.201128578779610.1080/07391102.2011.10508606 21294589
    [Google Scholar]
  71. AmaroR.E. BaronR. McCammonJ.A. An improved relaxed complex scheme for receptor flexibility in computer-aided drug design.J. Comput. Aided Mol. Des.200822969370510.1007/s10822‑007‑9159‑2 18196463
    [Google Scholar]
  72. FajerM. BorrelliK. AbelR. WangL. Quantitatively accounting for protein reorganization in computer-aided drug design.J. Chem. Theory Comput.202319113080309010.1021/acs.jctc.3c00009 37219932
    [Google Scholar]
  73. ColeD.J. HortonJ.T. NelsonL. KurdekarV. The future of force fields in computer-aided drug design.Future Med. Chem.201911182359236310.4155/fmc‑2019‑0196 31544529
    [Google Scholar]
  74. SydowD. MorgerA. DrillerM. VolkamerA. TeachOpenCADD: A teaching platform for computer-aided drug design using open source packages and data.J. Cheminform.20191112910.1186/s13321‑019‑0351‑x 30963287
    [Google Scholar]
  75. CarlsonH.A. MasukawaK.M. McCammonJ.A. Method for including the dynamic fluctuations of a protein in computer-aided drug design.J. Phys. Chem. A199910349102131021910.1021/jp991997z
    [Google Scholar]
  76. ÅqvistJ. MedinaC. SamuelssonJ.E. A new method for predicting binding affinity in computer-aided drug design.Protein Eng. Des. Sel.19947338539110.1093/protein/7.3.385 8177887
    [Google Scholar]
  77. BakerN.C. EkinsS. WilliamsA.J. TropshaA. A bibliometric review of drug repurposing.Drug Discov. Today201823366167210.1016/j.drudis.2018.01.018 29330123
    [Google Scholar]
  78. TaleleT. KhedkarS. RigbyA. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic.Curr. Top. Med. Chem.201010112714110.2174/156802610790232251 19929824
    [Google Scholar]
  79. PatilS. ShankarH. Transforming healthcare: Harnessing the power of AI in the modern era.Int J Multidiscip Sci Arts202322607010.47709/ijmdsa.v2i1.2513
    [Google Scholar]
  80. SabeV.T. NtombelaT. JhambaL.A. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review.Eur. J. Med. Chem.202122411370510.1016/j.ejmech.2021.113705 34303871
    [Google Scholar]
  81. DeyG. An overview of drug repurposing: Review article.J Med Sci Clin Res2019723510.18535/jmscr/v7i2.12
    [Google Scholar]
  82. KaramanB. SipplW. Computational drug repurposing: Current trends.Curr. Med. Chem.201926285389540910.2174/0929867325666180530100332 29848268
    [Google Scholar]
  83. SamigulinaG. ZarinaS. Immune network technology on the basis of random forest algorithm for computer-aided drug design.Bioinformatics and Biomedical Engineering. RojasI. OrtuñoF. ChamSpringer2017506110.1007/978‑3‑319‑56148‑6_4
    [Google Scholar]
  84. OselusiS.O. DubeP. OdugbemiA.I. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials.Comput. Biol. Med.202416910792710.1016/j.compbiomed.2024.107927 38184864
    [Google Scholar]
  85. GayathiriE. PrakashP. KumaravelP. Computational approaches for modeling and structural design of biological systems: A comprehensive review.Prog. Biophys. Mol. Biol.2023185173210.1016/j.pbiomolbio.2023.08.002 37821048
    [Google Scholar]
  86. FaverJ.C. UcisikM.N. YangW. MerzK.M. Computer-aided drug design: Using numbers to your advantage.ACS Med. Chem. Lett.20134981281410.1021/ml4002634 24312700
    [Google Scholar]
  87. SalmanM.M. Al-ObaidiZ. KitchenP. LoretoA. BillR.M. Wade-MartinsR. Advances in applying computer-aided drug design for neurodegenerative diseases.Int. J. Mol. Sci.2021229468810.3390/ijms22094688 33925236
    [Google Scholar]
  88. BibiS. SakataK. Current status of computer-aided drug design for type 2 diabetes.Curr. Computeraided Drug Des.201612216717710.2174/1573409912666160426120709 27113465
    [Google Scholar]
  89. ZhangY. LuoM. WuP. WuS. LeeT.Y. BaiC. Application of computational biology and artificial intelligence in drug design.Int. J. Mol. Sci.202223211356810.3390/ijms232113568 36362355
    [Google Scholar]
  90. TsagkarisC. CorrieroA.C. RayanR.A. MoysidisD.V. PapazoglouA.S. AlexiouA. Success stories in computer-aided drug design.Computational Approaches in Drug Discovery, Development and Systems Pharmacology.Chapter 9Academic Press202323725310.1016/B978‑0‑323‑99137‑7.00001‑0
    [Google Scholar]
  91. AskariS. GhofraniA. TaherdoostH. Transforming drug design: Innovations in computer-aided discovery for biosimilar agents.BioMedInformatics2023341178119610.3390/biomedinformatics3040070
    [Google Scholar]
  92. ChakrabartyB.K. Integrated CAD by Optimization.ChamSpringer202210.1007/978‑3‑030‑99306‑1
    [Google Scholar]
  93. DasN.R. JenaG.K. Bioinformatics and CADD approaches in drug discovery.Biochemical and Molecular Pharmacology in Drug Discovery.Chapter 15Elsevier202431332110.1016/B978‑0‑443‑16013‑4.00015‑4
    [Google Scholar]
  94. TrenfieldS.J. AwadA. MadlaC.M. Shaping the future: Recent advances of 3D printing in drug delivery and healthcare.Expert Opin. Drug Deliv.201916101081109410.1080/17425247.2019.1660318 31478752
    [Google Scholar]
  95. Pinto-CoelhoL. How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications.Bioengineering (Basel)20231012143510.3390/bioengineering10121435 38136026
    [Google Scholar]
  96. AguP.C. ObuloseC.N. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications.Drug Dev. Res.2024852e2215910.1002/ddr.22159 38375772
    [Google Scholar]
  97. KhannaA. El BarachiM. JainS. KumarM. NayyarA. Empowering clinical decision making. Artificial Intelligence and machine learning in drug design and development. Khanna A, Barachi ME, Jain S, Kumar M, Nayyar A, Eds. John Wiley & Sons202410.1002/9781394234196
    [Google Scholar]
  98. DuttonR.W. StrojwasA.J. Perspectives on technology and technology-driven CAD.IEEE Trans. Comput. Aided Des. Integrated Circ. Syst.200019121544156010.1109/43.898831
    [Google Scholar]
  99. OlawadeD.B. AderintoN. OlatunjiG. KokoriE. David-OlawadeA.C. HadiM. Advancements and applications of artificial intelligence in cardiology: Current trends and future prospects.J Med Surg Public Health2024310010910.1016/j.glmedi.2024.100109
    [Google Scholar]
  100. KumariN. HasijaY. CADD:- Exploring the digital frontier in drug designing.3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)Phuket, Thailand202427227710.1109/ICCMSO61761.2024.00062
    [Google Scholar]
  101. BrydenD. CAD and Rapid Prototyping for Product Design.Laurence King Publishing2014
    [Google Scholar]
  102. BalazicM. KopacJ. Improvements of medical implants based on modern materials and new technologies.J Achiev Mater Manuf Eng20072523134
    [Google Scholar]
  103. ThakurA. MehtaV. NaguP. GoutamK. Computer-Aided Drug Design: QSAR, Molecular Docking, Virtual Screening, Homology and Pharmacophore Modeling.Berlin, BostonWalter de Gruyter2024
    [Google Scholar]
  104. KumarS. ChananaA. SumanN. Various artificial intelligence models in pharmacy practice and drug development: A brief review.Adv Pharm J202491162410.31024/apj.2024.9.1.3
    [Google Scholar]
  105. BajorathJ. Integration of virtual and high-throughput screening.Nat. Rev. Drug Discov.200211188289410.1038/nrd941 12415248
    [Google Scholar]
  106. MacarronR. BanksM.N. BojanicD. Impact of high-throughput screening in biomedical research.Nat. Rev. Drug Discov.201110318819510.1038/nrd3368 21358738
    [Google Scholar]
  107. MengX.Y. ZhangH.X. MezeiM. CuiM. Molecular docking: A powerful approach for structure-based drug discovery.Curr. Computeraided Drug Des.20117214615710.2174/157340911795677602 21534921
    [Google Scholar]
  108. KitchenD.B. DecornezH. FurrJ.R. BajorathJ. Docking and scoring in virtual screening for drug discovery: Methods and applications.Nat. Rev. Drug Discov.200431193594910.1038/nrd1549 15520816
    [Google Scholar]
  109. AliperA. PlisS. ArtemovA. UlloaA. MamoshinaP. ZhavoronkovA. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data.Mol. Pharm.20161372524253010.1021/acs.molpharmaceut.6b00248 27200455
    [Google Scholar]
  110. Regassa HundeB. Debebe WoldeyohannesA. Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing.Results Eng20221410047810.1016/j.rineng.2022.100478
    [Google Scholar]
  111. CamposM.R.S. BojórquezN.C.Q. Traditional and novel computer-aided drug design (CADD) approaches in the anticancer drug discovery process.Curr. Cancer Drug Targets202323533334510.2174/1568009622666220705104249 35792126
    [Google Scholar]
  112. AundhiaC. ParmarG. TaleleC. ShahN. TaleleD. Impact of artificial intelligence on drug development and delivery.Curr. Top. Med. Chem.202410.2174/0115680266324522240725053634 39136506
    [Google Scholar]
  113. AroraA. KaurS. SinghA. Challenges and emerging problems in CADD.Drug Delivery Systems Using Quantum Computing. MalviyaR. SundramS. MeenakshiD.U. 202440744110.1002/9781394159338.ch14
    [Google Scholar]
  114. ZhaoL. CiallellaH.L. AleksunesL.M. ZhuH. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug Discov. Today20202591624163810.1016/j.drudis.2020.07.005 32663517
    [Google Scholar]
  115. NadaH. KimS. JaeminC. From pixels to druggable leads: A CADD strategy for the design and synthesis of potent DDR1 inhibitors.Comput. Methods Programs Biomed.202425410831810.1016/j.cmpb.2024.108318 38991374
    [Google Scholar]
/content/journals/cai/10.2174/0129503752321279241126091807
Loading
/content/journals/cai/10.2174/0129503752321279241126091807
Loading

Data & Media loading...

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