Skip to content
2000
Volume 22, Issue 4
  • ISSN: 1570-1638
  • E-ISSN: 1875-6220

Abstract

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the increasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polypharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided assistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effective drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the descriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cddt/10.2174/0115701638364199250123062248
2025-02-10
2025-07-28

  • From This Site

    /content/journals/cddt/10.2174/0115701638364199250123062248
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.01033099022
     [Google Scholar]
  2. ZhangL. TanJ. HanD. ZhuH. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery.Drug Discov. Today201722111680168510.1016/j.drudis.2017.08.01028881183
     [Google Scholar]
  3. XuY. LiuX. CaoX. HuangC. LiuE. QianS. LiuX. WuY. DongF. QiuC.W. QiuJ. HuaK. SuW. WuJ. XuH. HanY. FuC. YinZ. LiuM. RoepmanR. DietmannS. VirtaM. KengaraF. ZhangZ. ZhangL. ZhaoT. DaiJ. YangJ. LanL. LuoM. LiuZ. AnT. ZhangB. HeX. CongS. LiuX. ZhangW. LewisJ.P. TiedjeJ.M. WangQ. AnZ. WangF. ZhangL. HuangT. LuC. CaiZ. WangF. ZhangJ. Artificial intelligence: A powerful paradigm for scientific research.Innovation (Camb.)20212410017910.1016/j.xinn.2021.10017934877560
     [Google Scholar]
  4. ZhuangD. IbrahimA.K. Deep learning for drug discovery: A study of identifying high efficacy drug compounds using a cascade transfer learning approach.Appl. Sci.20211117777210.3390/app11177772
     [Google Scholar]
  5. PuL. NaderiM. LiuT. WuH.C. MukhopadhyayS. BrylinskiM. eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates.BMC Pharmacol. Toxicol.2019201210.1186/s40360‑018‑0282‑630621790
     [Google Scholar]
  6. ReesC. The Ethics of Artificial Intelligence.Unimagined Futures – ICT Opportunities and Challenges CRC Press2020556910.1007/978‑3‑030‑64246‑4_5
     [Google Scholar]
  7. ZhongF. XingJ. LiX. LiuX. FuZ. XiongZ. LuD. WuX. ZhaoJ. TanX. LiF. LuoX. LiZ. ChenK. ZhengM. JiangH. Artificial intelligence in drug design.Sci. China Life Sci.201861101191120410.1007/s11427‑018‑9342‑230054833
     [Google Scholar]
  8. BrownN. ErtlP. LewisR. LukschT. RekerD. SchneiderN. Artificial intelligence in chemistry and drug design.J. Comput. Aided Mol. Des.202034770971510.1007/s10822‑020‑00317‑x32468207
     [Google Scholar]
  9. RekerD. Practical considerations for active machine learning in drug discovery.Drug Discov. Today. Technol.201932-33737910.1016/j.ddtec.2020.06.00133386097
     [Google Scholar]
  10. BenekeF. MackenrodtM.O. Artificial intelligence and collusion.IIC Int. Rev. Intellect. Prop. Compet. Law2019501109134
     [Google Scholar]
  11. SteelsL. BrooksR. The Artificial Life Route to Artificial Intelligence. Building Embodied, Situated Agents.1st edRoutledge201810.4324/9781351001885
     [Google Scholar]
  12. BieleckiA. Foundations of artificial neural networks.Models of Neurons and Perceptrons: Selected Problems and Challenges.SpringerCham20201528
     [Google Scholar]
  13. KalyaneD. SanapG. PaulD. ShenoyS. AnupN. PolakaS. TambeV. TekadeR.K. Artificial intelligence in the pharmaceutical sector: Current scene and future prospect.The Future of Pharmaceutical Product Development and Research (Tekade, RK).Elsevier20207310710.1016/B978‑0‑12‑814455‑8.00003‑7
     [Google Scholar]
  14. da SilvaI.N. SpattiD.H. FlauzinoR.A. Bartocci LiboniL.H. dos Reis AlvesS.F. Artificial Neural Networks.1st edSpringer Cham201710.1007/978‑3‑319‑43162‑8
     [Google Scholar]
  15. MedskerL. JainL.C. Eds.; Recurrent Neural Networks: Design and Applications.1st edCRC Press199910.1201/9781420049176
     [Google Scholar]
  16. HänggiM MoschytzGS Cellular Neural Networks: Analysis, Design and OptimizationSpringer New York200010.1007/978‑1‑4757‑3220‑7
     [Google Scholar]
  17. DuchW. SwaminathanK. MellerJ. Artificial intelligence approaches for rational drug design and discovery.Curr. Pharm. Des.200713141497150810.2174/13816120778076595417504169
     [Google Scholar]
  18. BlasiakA. KhongJ. KeeT. CURATE.AI: Optimizing personalized medicine with artificial intelligence.SLAS Technol.20202529510510.1177/247263031989031631771394
     [Google Scholar]
  19. BaronzioG. ParmarG. BaronzioM. Overview of methods for overcoming hindrance to drug delivery to tumors, with special attention to tumor interstitial fluid.Front. Oncol.2015516510.3389/fonc.2015.0016526258072
     [Google Scholar]
  20. KozaJ.R. BennettF.H. AndreD. KeaneM.A. Automated design of both the topology and sizing of analog electrical circuits using genetic programming.Artificial Intelligence in Design ’96. GeroJ.S. SudweeksF. DordrechtSpringer199615117010.1007/978‑94‑009‑0279‑4_9
     [Google Scholar]
  21. KitchinR. McArdleG. What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets.Big Data Soc.201631205395171663113010.1177/2053951716631130
     [Google Scholar]
  22. BhadaniA JothimaniD. Big Data: Challenges, opportunities and realities.ArXiv2017
     [Google Scholar]
  23. LaroseD.T. LaroseC.D. Data mining and predictive analytics.2nd edWiley Publishing2015
     [Google Scholar]
  24. FernándezA. del RíoS. ChawlaN.V. HerreraF. An insight into imbalanced Big Data classification: Outcomes and challenges.Complex Intell. Syst.20173210512010.1007/s40747‑017‑0037‑9
     [Google Scholar]
  25. HasaninT. KhoshgoftaarT.M. LeevyJ.L. BauderR.A. Severely imbalanced Big Data challenges: Investigating data sampling approaches.J. Big Data20196110710.1186/s40537‑019‑0274‑4
     [Google Scholar]
  26. NathA. SubbiahK. Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.Comput. Biol. Chem.201559Pt A10111010.1016/j.compbiolchem.2015.09.01126433483
     [Google Scholar]
  27. NathA. KarthikeyanS. Enhanced prediction and characterization of CDK inhibitors using optimal class distribution.Interdiscip. Sci.20179229230310.1007/s12539‑016‑0151‑126879961
     [Google Scholar]
  28. WeiQ. DunbrackR.L.Jr The role of balanced training and testing data sets for binary classifiers in bioinformatics.PLoS One201387e6786310.1371/journal.pone.006786323874456
     [Google Scholar]
  29. NathA. SubbiahK. The role of pertinently diversified and balanced training as well as testing data sets in achieving the true performance of classifiers in predicting the antifreeze proteins.Neurocomputing.201827229430510.1016/j.neucom.2017.07.004
     [Google Scholar]
  30. TurkiT. TaguchiY. Machine learning algorithms for predicting drugs–tissues relationships.Expert Syst. Appl.201912716718610.1016/j.eswa.2019.02.013
     [Google Scholar]
  31. RantanenJ. KhinastJ. The future of pharmaceutical manufacturing sciences.J. Pharm. Sci.2015104113612363810.1002/jps.2459426280993
     [Google Scholar]
  32. PopovaM. IsayevO. TropshaA. Deep reinforcement learning for de novo drug design.Sci. Adv.201847eaap788510.1126/sciadv.aap788530050984
     [Google Scholar]
  33. TaroniJ.N. GraysonP.C. HuQ. EddyS. KretzlerM. MerkelP.A. GreeneC.S. MultiPLIER: A transfer learning framework for transcriptomics reveals systemic features of rare disease.Cell Syst.201985380394.e410.1016/j.cels.2019.04.00331121115
     [Google Scholar]
  34. LiL. HeX. BorgwardtK. Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.BMC Syst. Biol.201812S4Suppl. 45510.1186/s12918‑018‑0569‑729745839
     [Google Scholar]
  35. WengY. LinC. ZengX. LiangY. Drug Target interaction prediction using multi-task learning and co-attention.Proceeding of the IEEE international conference bioinformatics and biomedicine (BIBM)San Diego, CA, USA, 2019, pp. 528-533.10.1109/BIBM47256.2019.8983254
     [Google Scholar]
  36. HanL. ZhangY. Learning multi-level task groups in multi-task learning.AAAI Conf. Artif. Intell. 201529126384410.1609/aaai.v29i1.9581
     [Google Scholar]
  37. KadurinI. RothwellS. FerronL. MeyerO. DolphinA. Investigation of the proteolytic cleavage of α2δ subunits: a mechanistic switch from nhibition to activation of voltage-gated calcium channels?Biophys. J.20171123244a10.1016/j.bpj.2016.11.1335
     [Google Scholar]
  38. Al-SafariniM.Y. El-SayedH.H. The role of artificial intelligence in revealing the results of the interaction of biological materials with each other or with chemicals.Mater. Today Proc.20214564954495910.1016/j.matpr.2021.01.387
     [Google Scholar]
  39. UnterthinerT MayrA KlambauerG HochreiterS. Toxicity Prediction using deep learning.ArXiv2015
     [Google Scholar]
  40. AvdagicZ. Begic FazlicL. KonjicijaS. Optimized detection of tar content in the manufacturing process using adaptive neuro-fuzzy inference systems.Stud. Health Technol. Inform.200915061561919745385
     [Google Scholar]
  41. ZhavoronkovA IvanenkovYA AliperA Veselov M.S. Aladinskiy V.A. Aladinskaya A.V. Terentiev V.A. Polykovskiy D.A Kuznetsov M.D Deep learning enables rapid identification of potent DDR1 kinase inhibitors.Nat. Biotechnol.20193791038 10.1038/s41587‑019‑0224‑x
     [Google Scholar]
  42. LeeH. KimW. Comparison of target features for predicting drug-target interactions by deep neural network based on large-scale drug-induced transcriptome data.Pharmaceutics201911837710.3390/pharmaceutics1108037731382356
     [Google Scholar]
  43. PutinE. AsadulaevA. IvanenkovY. AladinskiyV. Sanchez-LengelingB. Aspuru-GuzikA. ZhavoronkovA. Reinforced adversarial neural computer for de novo molecular design.J. Chem. Inf. Model.20185861194120410.1021/acs.jcim.7b0069029762023
     [Google Scholar]
  44. DashS. ShakyawarS.K. SharmaM. KaushikS. Big data in healthcare: Management, analysis and future prospects.J. Big Data2019615410.1186/s40537‑019‑0217‑0
     [Google Scholar]
  45. GrelckC. Niewiadomska-SzynkiewiczE. AldinucciM. BraccialiA. LarssonE. Why high-performance modelling and simulation for big data applications matters.High-Performance Modelling and Simulation for Big Data ApplicationsSpringer Cham.1st ed González-VélezH. KołodziejJ. 2019135
     [Google Scholar]
  46. LakeF. Artificial intelligence in drug discovery: What is new, and what is next?Future. Drug. Discov.201912FDD1910.4155/fdd‑2019‑0025
     [Google Scholar]
  47. RameshA.N. KambhampatiC. MonsonJ.R.T. DrewP.J. Artificial intelligence in medicine.Ann. R. Coll. Surg. Engl.200486533433810.1308/14787080429015333167
     [Google Scholar]
  48. MakK.K. PichikaM.R. Artificial intelligence in drug development: present status and future prospects.Drug Discov. Today201924377378010.1016/j.drudis.2018.11.01430472429
     [Google Scholar]
  49. VyasM. ThakurS. RiyazB. BansalK. TomarB. MishraV. Artificial intelligence: The beginning of a new era in pharmacy profession.Asian J. Pharm.2018127276
     [Google Scholar]
  50. SellwoodM.A. AhmedM. SeglerM.H.S. BrownN. Artificial intelligence in drug discovery.Future Med. Chem.201810172025202810.4155/fmc‑2018‑021230101607
     [Google Scholar]
  51. ZhuH. Big data and artificial intelligence modeling for drug discovery.Annu. Rev. Pharmacol. Toxicol.202060157358910.1146/annurev‑pharmtox‑010919‑02332431518513
     [Google Scholar]
  52. CiallellaH.L. ZhuH. Advancing computational toxicology in the big data era by artificial intelligence: Data-driven and mechanism-driven modeling for chemical toxicity.Chem. Res. Toxicol.201932453654710.1021/acs.chemrestox.8b0039330907586
     [Google Scholar]
  53. ChanH.C.S. ShanH. DahounT. VogelH. YuanS. Advancing drug discovery via artificial intelligence.Trends Pharmacol. Sci.201940859260410.1016/j.tips.2019.06.00431320117
     [Google Scholar]
  54. BrownN. In Silico Medicinal Chemistry: Computational Methods to Support Drug Design(Theoretical and Computational Chemistry Series). Royal Society of Chemistry201510.1039/9781782622604
     [Google Scholar]
  55. PereiraJ.C. CaffarenaE.R. dos SantosC.N. Boosting docking-based virtual screening with deep learning.J. Chem. Inf. Model.201656122495250610.1021/acs.jcim.6b0035528024405
     [Google Scholar]
  56. FirthN.C. AtrashB. BrownN. BlaggJ. MOARF, an integrated workflow for multiobjective optimization: Implementation, synthesis, and biological evaluation.J. Chem. Inf. Model.20155561169118010.1021/acs.jcim.5b0007326054755
     [Google Scholar]
  57. JainN. GuptaS. SapreN. SapreN.S. In silico de novo design of novel NNRTIs: A bio-molecular modelling approach.RSC Advances2015519148141482710.1039/C4RA15478A
     [Google Scholar]
  58. WangY. GuoY. KuangQ. PuX. JiY. ZhangZ. LiM. A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach.J. Comput. Aided Mol. Des.201529434936010.1007/s10822‑014‑9827‑y25527073
     [Google Scholar]
  59. KingR.D. HirstJ.D. SternbergM.J. Comparison of artificial intelligence methods for modelling pharmaceutical QSARs.Appl. Artif. Intell.19959221323310.1080/08839519508945474
     [Google Scholar]
  60. NguyenG. DlugolinskyS. BobákM. TranV. López GarcíaÁ. HerediaI. MalíkP. HluchýL. Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey.Artif. Intell. Rev.20195217712410.1007/s10462‑018‑09679‑z
     [Google Scholar]
  61. PaszkeA. GrossS. MassaF. LererA. LarochelleH. BeygelzimerA. BradburyJ. ChananG. KilleenT. PyTorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems. WallachH. LarochelleH. BeygelzimerA. Curran Associates Inc2009
     [Google Scholar]
  62. LysenkoA. SharmaA. BoroevichK.A. TsunodaT. An integrative machine learning approach for prediction of toxicity-related drug safety.Life Sci. Alliance201816e20180009810.26508/lsa.20180009830515477
     [Google Scholar]
  63. YangY. HeffernanR. PaliwalK. LyonsJ. DehzangiA. SharmaA. WangJ. SattarA. ZhouY. SPIDER2: A package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks.Methods. Mol. Biol.20171484556310.1007/978‑1‑4939‑6406‑2_627787820
     [Google Scholar]
  64. MatlockM.K. HughesT.B. SwamidassS.J. XenoSite server: A web-available site of metabolism prediction tool.Bioinformatics20153171136113710.1093/bioinformatics/btu76125411327
     [Google Scholar]
  65. ŠíchoM. de Bruyn KopsC. StorkC. SvozilD. KirchmairJ. FAME 2: Simple and effective machine learning model of cytochrome P450 Regioselectivity.J. Chem. Inf. Model.20175781832184610.1021/acs.jcim.7b0025028782945
     [Google Scholar]
  66. RydbergP. GloriamD.E. ZaretzkiJ. BrenemanC. OlsenL. SMARTCyp: A 2D method for prediction of cytochrome P450-mediated drug metabolism.ACS Med. Chem. Lett.2010139610010.1021/ml100016x24936230
     [Google Scholar]
  67. BlaschkeT. Arús-PousJ. ChenH. MargreitterC. TyrchanC. EngkvistO. PapadopoulosK. PatronovA. REINVENT 2.0: An AI tool for de novo drug design.J. Chem. Inf. Model.202060125918592210.1021/acs.jcim.0c0091533118816
     [Google Scholar]
  68. YuanQ. YangY. Sequence-based predictions of residues that bind proteins and peptides.Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics.World Scientific202323726310.1142/9789811258589_0009
     [Google Scholar]
  69. ChoA. No room for error.Science2020369650013013310.1126/science.369.6500.13032646981
     [Google Scholar]
  70. KimS. ChenJ. ChengT. Gindulyte A. He J. He S. LiQ. Shoemaker B.A. Thiessen P.A. Yu B. Zaslavsky L. PubChem 2023 update.Nucleic. Acids. Res.201151D1373D138010.1093/nar/gkac956
     [Google Scholar]
  71. ChoA. SDM: A server for predicting effects of mutations on protein stability and malfunction.Nucleic. Acids. Res.2011392W215W222
     [Google Scholar]
  72. AwaleM. ReymondJ.L. Polypharmacology browser PPB2: Target prediction combining nearest neighbors with machine learning.J. Chem. Inf. Model.2019591101710.1021/acs.jcim.8b0052430558418
     [Google Scholar]
  73. FeinbergE.N. SurD. WuZ. HusicB.E. MaiH. LiY. SunS. YangJ. RamsundarB. PandeV.S. PotentialNet for molecular property prediction.ACS Cent. Sci.20184111520153010.1021/acscentsci.8b0050730555904
     [Google Scholar]
  74. RapicavoliR.V. AlaimoS. FerroA. PulvirentiA. Computational methods for drug repurposing.Adv. Exp. Med. Biol.2022136111914110.1007/978‑3‑030‑91836‑1_735230686
     [Google Scholar]
  75. de GlasN.A. BastiaannetE. EngelsC.C. de CraenA.J.M. PutterH. van de VeldeC.J.H. HurriaA. LiefersG.J. PortieljeJ.E.A. Validity of the online PREDICT tool in older patients with breast cancer: A population-based study.Br. J. Cancer2016114439540010.1038/bjc.2015.46626783995
     [Google Scholar]
  76. NagS BaidyaATK MandalA MathewAT Deep learning tools for advancing drug discovery and development.Biotech.2022125110
     [Google Scholar]
  77. Sanchez-LengelingB. OuteiralC. GuimaraesG.L. Aspuru-GuzikA. Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC).ChemRxiv2017
     [Google Scholar]
  78. WójcikowskiM. ZielenkiewiczP. SiedleckiP. Open Drug Discovery Toolkit (ODDT): A new open-source player in the drug discovery field.J. Cheminform.2015712610.1186/s13321‑015‑0078‑226101548
     [Google Scholar]
  79. DurrantJ.D. McCammonJ.A. NNScore 2.0: A neural-network receptor-ligand scoring function.J. Chem. Inf. Model.201151112897290310.1021/ci200388922017367
     [Google Scholar]
  80. DuvenaudD.K. MaclaurinD. Aguilera-IparraguirreJ. Gómez-BombarelliR. HirzelT.D. Aspuru-GuzikA. AdamsR.P. Convolutional networks on graphs for learning molecular fingerprints.Advances in neural information processing systems CortesC. LawrenceN. LeeD.D. GarnettR. LawrenceN.D. SugiyamaM. The MIT Press2015
     [Google Scholar]
  81. JinW. BarzilayR. JaakkolaT. Junction tree variational autoencoder for molecular graph generation.35th International conference on machine learning.Stockholmsmässan, Stockholm, Sweden 2018 pp. 2323–32
     [Google Scholar]
  82. UrbanG. SubrahmanyaN. BaldiP. Inner and outer recursive neural networks for chemoinformatics applications.J. Chem. Inf. Model.201858220721110.1021/acs.jcim.7b0038429320180
     [Google Scholar]
  83. StorkC. ChenY. ŠíchoM. KirchmairJ. Hit dexter 2.0: Machine-learning models for the prediction of frequent hitters.J. Chem. Inf. Model.20195931030104310.1021/acs.jcim.8b0067730624935
     [Google Scholar]
  84. WangC. ZhangY. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.J. Comput. Chem.201738316917710.1002/jcc.2466727859414
     [Google Scholar]
  85. XieZ. WangH. HanS. SchoenfeldE. YeF. DeepVS: A deep learning approach for RF-based vital signs sensing.Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health InformaticsNorthbrook, Illinois USA, 2022, pp. 1-510.1145/3535508.3545554
     [Google Scholar]
  86. MayrA. KlambauerG. UnterthinerT. HochreiterS. DeepTox: Toxicity prediction using deep learning.Front. Environ. Sci.201638010.3389/fenvs.2015.00080
     [Google Scholar]
  87. GhasemiF. MehridehnaviA. FassihiA. Pérez-SánchezH. Deep neural network in QSAR studies using deep belief network.Appl. Soft Comput.20186225125810.1016/j.asoc.2017.09.040
     [Google Scholar]
  88. MaW. ZhangS. LiZ. JiangM. WangS. GuoN. LiY. BiX. JiangH. WeiZ. Predicting drug-target affinity by learning protein knowledge from biological networks.IEEE J. Biomed. Health Inform.20232742128213710.1109/JBHI.2023.324030537018115
     [Google Scholar]
  89. D’SouzaS. PremaK.V. BalajiS. ShahR. Deep learning-based modeling of drug–target interaction prediction incorporating binding site information of proteins.Interdiscip. Sci.202315230631510.1007/s12539‑023‑00557‑z36967455
     [Google Scholar]
  90. KrishnanA. VinodD. Graph Convolutional Capsule Regression (GCCR): A model for accelerated filtering of novel potential candidates for SARS-CoV-2 based on binding affinity.Curr. Computeraided. Drug. Des.2024201334110.2174/157340991966623033108395337005531
     [Google Scholar]
  91. AmanatidisD. VaitsiK. DossisM.F. Deep neural network applications for bioinformatics.Computer Networks and Social Media Conference (SEEDA-CECNSM)Ioannina, Greece, 2022, pp.1-9
     [Google Scholar]
  92. ColeyC.W. BarzilayR. GreenW.H. JaakkolaT.S. JensenK.F. Convolutional embedding of attributed molecular graphs for physical property prediction.J. Chem. Inf. Model.20175781757177210.1021/acs.jcim.6b0060128696688
     [Google Scholar]
  93. SteinerS. WolfJ. GlatzelS. AndreouA. GrandaJ.M. KeenanG. HinkleyT. Aragon-CamarasaG. KitsonP.J. AngeloneD. CroninL. Organic synthesis in a modular robotic system driven by a chemical programming language.Science20193636423eaav221110.1126/science.aav221130498165
     [Google Scholar]
  94. BhattamisraS.K. BanerjeeP. GuptaP. MayurenJ. PatraS. CandasamyM. Artificial intelligence in pharmaceutical and healthcare research.Big. data. cogn.2023711010.3390/bdcc7010010
     [Google Scholar]
  95. AhmadianM. AhmadianS. AhmadiM. RDERL: Reliable deep ensemble reinforcement learning-based recommender system.Knowl. Base. Syst.202326311028910.1016/j.knosys.2023.110289
     [Google Scholar]
  96. Gómez-BombarelliR. WeiJ.N. DuvenaudD. Hernández-LobatoJ.M. Sánchez-LengelingB. SheberlaD. Aguilera-IparraguirreJ. HirzelT.D. AdamsR.P. Aspuru-GuzikA. Automatic chemical design using a data-driven continuous representation of molecules.ACS Cent. Sci.20184226827610.1021/acscentsci.7b0057229532027
     [Google Scholar]
  97. 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/s41586‑021‑03819‑234265844
     [Google Scholar]
  98. SeniorA.W. EvansR. JumperJ. KirkpatrickJ. SifreL. GreenT. QinC. ŽídekA. NelsonA.W.R. BridglandA. PenedonesH. PetersenS. SimonyanK. CrossanS. KohliP. JonesD.T. SilverD. KavukcuogluK. HassabisD. Improved protein structure prediction using potentials from deep learning.Nature2020577779270671010.1038/s41586‑019‑1923‑731942072
     [Google Scholar]
  99. KumarK. KumarP. DebD. UnguresanM.L. MuresanV. Artificial intelligence and machine learning based intervention in medical infrastructure: A review and future trends.Healthcare202311220710.3390/healthcare1102020736673575
     [Google Scholar]
  100. SchneiderP. WaltersW.P. PlowrightA.T. SierokaN. ListgartenJ. GoodnowR.A.Jr FisherJ. JansenJ.M. DucaJ.S. RushT.S. ZentgrafM. HillJ.E. KrutoholowE. KohlerM. BlaneyJ. FunatsuK. LuebkemannC. SchneiderG. Rethinking drug design in the artificial intelligence era.Nat. Rev. Drug Discov.202019535336410.1038/s41573‑019‑0050‑331801986
     [Google Scholar]
  101. BenderA. Cortés-CirianoI. Artificial intelligence in drug discovery: What is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet.Drug. Discov. Today202126251152410.1016/j.drudis.2020.12.00933346134
     [Google Scholar]
  102. PabrinkisA. BucherA. KamuntaviciusG. PratA. BastasO. JocysZ. TalR. KnuffC.D. System and method for de novo drug discovery.US Patent US11615324B22023
  103. LeeF. SteckbeckJ.D. HolsteH. Artificial intelligence engine architecture for generating candidate drugs.US patent US20210295955A12022
  104. StojevicV WrightD FrostJM Computer implemented method and system for small molecule drug discovery. WO patent WO2022043690A12022
  105. LeeF. SteckbeckJ.D. HolsteH. Generating enhanced graphical user interfaces for presentation of anti-infective design spaces for selecting drug candidates.US Patent US11403316B22022
  106. StojevicV. ShakerN. BalM. Tensor network machine learning system.US Patent 20210081804A12021
  107. TriendlH BalM FrostJM PhillipsL ChantzisA SimpsonG StojevicV ShakerN GraigM BashirU AssmannM Machine learning based methods of analysing drug-like molecules. WO patent WO2020016579A22020
  108. WeiG Systems and methods for drug design and discovery comprising applications of machine learning with differential geometric modelling.WO patent WO2019191777A12019
  109. VatanseverS. SchlessingerA. WackerD. KaniskanH.Ü. JinJ. ZhouM.M. ZhangB. Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions.Med. Res. Rev.20214131427147310.1002/med.2176433295676
     [Google Scholar]
  110. StokesJ.M. YangK. SwansonK. JinW. Cubillos-RuizA. DonghiaN.M. MacNairC.R. FrenchS. CarfraeL.A. Bloom-AckermannZ. TranV.M. Chiappino-PepeA. BadranA.H. AndrewsI.W. ChoryE.J. ChurchG.M. BrownE.D. JaakkolaT.S. BarzilayR. CollinsJ.J. A deep learning approach to antibiotic discovery.Cell2020181247548310.1016/j.cell.2020.04.00132302574
     [Google Scholar]
  111. MamoshinaP. VieiraA. PutinE. ZhavoronkovA. Applications of deep learning in biomedicine.Mol. Pharm.20161351445145410.1021/acs.molpharmaceut.5b0098227007977
     [Google Scholar]
  112. PreuerK. KlambauerG. RippmannF. HochreiterS. UnterthinerT. Interpretable deep learning in drug discovery.Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. SamekW. MontavonG. VedaldiA. HansenL. MüllerK.R. Springer Cham201910.1007/978‑3‑030‑28954‑6_18
     [Google Scholar]
  113. RamsundarB. LiuB. WuZ. VerrasA. TudorM. SheridanR.P. PandeV. Is multitask deep learning practical for pharma?J. Chem. Inf. Model.20175782068207610.1021/acs.jcim.7b0014628692267
     [Google Scholar]
  114. GraceK. SalvatierJ. DafoeA. ZhangB. EvansO. Viewpoint: When will AI exceed human performance? Evidence from AI experts.J. Artif. Intell. Res.20186272975410.1613/jair.1.11222
     [Google Scholar]
  115. YangX. WangY. ByrneR. SchneiderG. YangS. Concepts of artificial intelligence for computer-assisted drug discovery.Chem. Rev.201911918105201059410.1021/acs.chemrev.8b0072831294972
     [Google Scholar]
  116. Altae-TranH. RamsundarB. PappuA.S. PandeV. Low data drug discovery with one-shot learning.ACS Cent. Sci.20173428329310.1021/acscentsci.6b0036728470045
     [Google Scholar]
  117. HesslerG. BaringhausK.H. Artificial intelligence in drug design.Molecules20182310252010.3390/molecules2310252030279331
     [Google Scholar]
  118. RehmanA.U. LiM. WuB. AliY. RasheedS. ShaheenS. LiuX. LuoR. ZhangJ. Role of artificial intelligence in revolutionizing drug discovery.Fundam. Res.202410.1016/j.fmre.2024.04.021
     [Google Scholar]
  119. SuriyaampornP. PamornpathomkulB. PatrojanasophonP. NgawhirunpatT. RojanarataT. OpanasopitP. The artificial intelligence-powered new era in pharmaceutical research and development: A review.AAPS Pharm. Sci. Tech.202425618810.1208/s12249‑024‑02901‑y39147952
     [Google Scholar]
  120. TripathiA. MisraK. DhanukaR. SinghJ.P. Artificial intelligence in accelerating drug discovery and development.Recent Pat. Biotechnol.202317192310.2174/187220831666622080215112935927896
     [Google Scholar]
  121. LiebmanM. The role of artificial intelligence in drug discovery and development.Chem. Int.2022441161910.1515/ci‑2022‑0105
     [Google Scholar]
  122. RohallS.L. AuchL. GableJ. GoraJ. JansenJ. LuY. MartinE. Pancost-HeidebrechtM. ShirleyB. StieflN. LindvallM. An artificial intelligence approach to proactively inspire drug discovery with recommendations.J. Med. Chem.202063168824883410.1021/acs.jmedchem.9b0213032101427
     [Google Scholar]
/content/journals/cddt/10.2174/0115701638364199250123062248
Loading
Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development
Current Drug Discovery Technologies 22, E15701638364199 (2025); https://doi.org/10.2174/0115701638364199250123062248
/content/journals/cddt/10.2174/0115701638364199250123062248
/content/journals/cddt/10.2174/0115701638364199250123062248
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): algorithm; artificial intelligence; deep learning; Drug design; drug development; drug discovery; machine learning

Most Cited Most Cited RSS feed

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