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Volume 24, Issue 1
  • ISSN: 1570-159X
  • E-ISSN: 1875-6190
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Abstract

Introduction

In the medicinal chemistry (MC) field, artificial intelligence (AI) has been used to establish quantitative structure-activity relationship (QSAR) classification models, virtual screening, drug discovery, drug design, and so on. In this investigation, MC AI studies (AI-MC) (from 2001-2023) underwent quantitative and qualitative modeling analyses.

Methods

Using a hybrid research strategy incorporating content analyses and bibliometric methods, we retrospectively analysed the AI-MC literature using a bibliometrix package (R software) combined with CiteSpace V and VOSviewer programs.

Results

Between 2001 and 2023, AI-MC articles were published in 92 countries or regions, with China and the United States leading in the number of publications. Also, 196 affiliations were added to AI-MC research; the CHINESE ACADEMY OF SCIENCES contributed the most. Reference clusters were categorized as follows: (1) QSAR, (2) virtual screening, (3) drug discovery, (4) drug design. Predictive model (2020-2021), molecular fingerprints (2021-2023) and scoring function (2021-2023) reflected research frontier keywords. As we look to the future, the ongoing progress and innovation in technology herald the promising development of multimodal and large language models (LLMs) within the realm of MC.

Discussion

The integration of AI into MC has significantly transformed the landscape of drug development. AI techniques, particularly machine learning, and deep learning algorithms, have demonstrated remarkable potential in accelerating the discovery and optimization of new drugs. By leveraging large datasets and advanced computational models, AI enhances the efficiency of virtual screening, improves the accuracy of QSAR models, and facilitates the design of novel therapeutic agents. As the technology continues to advance, the development of multimodal and large language models (LLMs) is expected to further revolutionize this field, offering new opportunities for more precise and efficient drug design and discovery.

Conclusion

We comprehensively characterized the AI-MC field and determined future trends and hotspots. Importantly, we provided a dynamic oversight of the AI-MC literature and identified key upcoming research areas.

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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References

  1. LiangG.Q. DengJ. WuC.X. LiW.J. HuY.F. SongY.Q. YinX.C. HeQ. XiaoY.C. LiG.B. Design, synthesis, and biological evaluation of boron-containing β-lactamase inhibitors: Closed-loop education experiences in an undergraduate medicinal chemistry course.J. Chem. Educ.2023100280381010.1021/acs.jchemed.2c00692
     [Google Scholar]
  2. FernandesJ.P.S. The importance of medicinal chemistry knowledge in the clinical pharmacist’s education.Am. J. Pharm. Educ.2018822608310.5688/ajpe6083 29606703
     [Google Scholar]
  3. HannM.M. KeserüG.M. Finding the sweet spot: The role of nature and nurture in medicinal chemistry.Nat. Rev. Drug Discov.201211535536510.1038/nrd3701 22543468
     [Google Scholar]
  4. GromekS. BalunasM. Natural products as exquisitely potent cytotoxic payloads for antibody- drug conjugates.Curr. Top. Med. Chem.201514242822283410.2174/1568026615666141208111253 25487009
     [Google Scholar]
  5. Fosso WambaS. BawackR.E. GuthrieC. QueirozM.M. CarilloK.D.A. Are we preparing for a good AI society? A bibliometric review and research agenda.Technol. Forecast. Soc. Change202116412048210.1016/j.techfore.2020.120482
     [Google Scholar]
  6. JiangF. JiangY. ZhiH. DongY. LiH. MaS. WangY. DongQ. ShenH. WangY. Artificial intelligence in healthcare: Past, present and future.Stroke Vasc. Neurol.20172423024310.1136/svn‑2017‑000101 29507784
     [Google Scholar]
  7. LeeS. Raza ShahS.A. SeokW. MoonJ. KimK. Raza ShahS.H. An optimal network-aware scheduling technique for distributed deep learning in distributed HPC platforms.Electronics20231214302110.3390/electronics12143021
     [Google Scholar]
  8. BuccheriE. Dell’AquilaD. RussoM. ChiaramonteR. VecchioM. Appendicular skeletal muscle mass in older adults can be estimated with a simple equation using a few zero-cost variables.J. Geriatr. Phys. Ther.2024474E149E15810.1519/JPT.0000000000000420 39079022
     [Google Scholar]
  9. 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]
  10. 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]
  11. AzarA.T. El-MetwallyS.M. Decision tree classifiers for automated medical diagnosis.Neural Comput. Appl.2013237-82387240310.1007/s00521‑012‑1196‑7
     [Google Scholar]
  12. BuccheriE. Dell’AquilaD. RussoM. ChiaramonteR. MusumeciG. VecchioM. Can artificial intelligence simplify the screening of muscle mass loss?Heliyon202395e1632310.1016/j.heliyon.2023.e16323 37251872
     [Google Scholar]
  13. BatoolM. AhmadB. ChoiS. A structure-based drug discovery paradigm.Int. J. Mol. Sci.20192011278310.3390/ijms20112783 31174387
     [Google Scholar]
  14. YangX. WangY. ByrneR. SchneiderG. YangS. Concepts of artificial intelligence for computer-assisted drug discovery.Chem. Rev.201911918105201059410.1021/acs.chemrev.8b00728 31294972
     [Google Scholar]
  15. MerigóJ.M. Gil-LafuenteA.M. YagerR.R. An overview of fuzzy research with bibliometric indicators.Appl. Soft Comput.20152742043310.1016/j.asoc.2014.10.035
     [Google Scholar]
  16. MoraL. DeakinM. ReidA. Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities.Technol. Forecast. Soc. Change2019142566910.1016/j.techfore.2018.07.019
     [Google Scholar]
  17. WangS. ZhouH. ZhengL. ZhuW. ZhuL. FengD. WeiJ. ChenG. JinX. YangH. ShiX. LvX. Global trends in research of macrophages associated with acute lung injury over past 10 years: A bibliometric analysis.Front. Immunol.20211266953910.3389/fimmu.2021.669539 34093568
     [Google Scholar]
  18. SunH.L. BaiW. LiX.H. HuangH. CuiX.L. CheungT. SuZ.H. YuanZ. NgC.H. XiangY.T. Schizophrenia and inflammation research: A bibliometric analysis.Front. Immunol.20221390785110.3389/fimmu.2022.907851 35757702
     [Google Scholar]
  19. MaJ. SheridanR.P. LiawA. DahlG.E. SvetnikV. Deep neural nets as a method for quantitative structure-activity relationships.J. Chem. Inf. Model.201555226327410.1021/ci500747n 25635324
     [Google Scholar]
  20. RagozaM. HochuliJ. IdroboE. SunseriJ. KoesD.R. Protein–ligand scoring with convolutional neural networks.J. Chem. Inf. Model.201757494295710.1021/acs.jcim.6b00740 28368587
     [Google Scholar]
  21. YangK. SwansonK. JinW.G. ColeyC. EidenP. GaoH. Guzman-PerezA. HopperT. KelleyB. MatheaM. PalmerA. SettelsV. JaakkolaT. JensenK. BarzilayR. Analyzing learned molecular representations for property prediction.J. Chem. Inf. Model.201959125304530510.1021/acs.jcim.9b01076 31814400
     [Google Scholar]
  22. LusciA. PollastriG. BaldiP. Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules.J. Chem. Inf. Model.20135371563157510.1021/ci400187y 23795551
     [Google Scholar]
  23. GuptaA. MüllerA.T. HuismanB.J.H. FuchsJ.A. SchneiderP. SchneiderG. Generative recurrent networks for de novo drug design.Mol. Inform.2018371-2170011110.1002/minf.201700111
     [Google Scholar]
  24. BrownN. FiscatoM. SeglerM.H.S. VaucherA.C. GuacaMol: Benchmarking Models for de novo molecular design.J. Chem. Inf. Model.20195931096110810.1021/acs.jcim.8b00839 30887799
     [Google Scholar]
  25. GawehnE. HissJ.A. SchneiderG. Deep learning in drug discovery.Mol. Inform.201635131410.1002/minf.201501008 27491648
     [Google Scholar]
  26. 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]
  27. JaegerS. FulleS. TurkS. Mol2vec: Unsupervised machine learning approach with chemical intuition.J. Chem. Inf. Model.2018581273510.1021/acs.jcim.7b00616 29268609
     [Google Scholar]
  28. PereiraJ.C. CaffarenaE.R. dos SantosC.N. Boosting docking-based virtual screening with deep learning.J. Chem. Inf. Model.201656122495250610.1021/acs.jcim.6b00355 28024405
     [Google Scholar]
  29. CherkasovA. MuratovE.N. FourchesD. VarnekA. BaskinI.I. CroninM. DeardenJ. GramaticaP. MartinY.C. TodeschiniR. ConsonniV. Kuz’minV.E. CramerR. BenigniR. YangC. RathmanJ. TerflothL. GasteigerJ. RichardA. TropshaA. QSAR modeling: Where have you been? Where are you going to?J. Med. Chem.201457124977501010.1021/jm4004285 24351051
     [Google Scholar]
  30. MoriwakiH. TianY.S. KawashitaN. TakagiT. Three-dimensional classification structure–activity relationship analysis using convolutional neural network.Chem. Pharm. Bull. (Tokyo)201967542643210.1248/cpb.c18‑00757 31061367
     [Google Scholar]
  31. AndradeC.H. PasqualotoK.F.M. FerreiraE.I. HopfingerA.J. 4D-QSAR: Perspectives in drug design.Molecules20101553281329410.3390/molecules15053281 20657478
     [Google Scholar]
  32. HameedR. KhanA. KhanS. PerveenS. Computational approaches towards kinases as attractive targets for anticancer drug discovery and development.Anticancer. Agents Med. Chem.201919559259810.2174/1871520618666181009163014 30306880
     [Google Scholar]
  33. KurniawanI. RosalindaM. IkhsanN. Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent.SAR QSAR Environ. Res.202031647749210.1080/1062936X.2020.1773534 32546117
     [Google Scholar]
  34. AmorosoN. QuartoS. La RoccaM. TangaroS. MonacoA. BellottiR. An explainability artificial intelligence approach to brain connectivity in Alzheimer’s disease.Front. Aging Neurosci.202315123806510.3389/fnagi.2023.1238065 37719873
     [Google Scholar]
  35. YaprakdalF. VarolA.M. A multivariate time series analysis of electrical load forecasting based on a hybrid feature selection approach and explainable deep learning.Appl. Sci.202313231294610.3390/app132312946
     [Google Scholar]
  36. AlMohimeedA. SaadR.M.A. MostafaS. El-RashidyN.M. FarragS. GaballahA. ElazizM.A. El-SappaghS. SalehH. Explainable artificial intelligence of multi-level stacking ensemble for detection of Alzheimer’s disease based on particle swarm optimization and the sub-scores of cognitive biomarkers.IEEE Access20231112317312319310.1109/ACCESS.2023.3328331
     [Google Scholar]
  37. KamalM.S. ChowdhuryL. NimmyS.F. Hasan RafiT.H. ChaeD-K. An Interpretable Framework for Identifying Cerebral Microbleeds and Alzheimer’s Disease Severity using Multimodal Data.2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2023 Sydney, Australia, 24-27 July 20231410.1109/EMBC40787.2023.10340088
     [Google Scholar]
  38. CalabreseV. ScapagniniG. RavagnaA. BellaR. ButterfieldD.A. CalvaniM. PennisiG. StellaG.A.M. Disruption of thiol homeostasis and nitrosative stress in the cerebrospinal fluid of patients with active multiple sclerosis: Evidence for a protective role of acetylcarnitine.Neurochem. Res.20032891321132810.1023/A:1024984013069 12938853
     [Google Scholar]
  39. CalabreseV. ColombritaC. GuaglianoE. SapienzaM. RavagnaA. CardileV. ScapagniniG. SantoroA.M. MangiameliA. ButterfieldD.A. StellaA.M.G. RizzarelliE. Protective effect of carnosine during nitrosative stress in astroglial cell cultures.Neurochem. Res.2005306-779780710.1007/s11064‑005‑6874‑8 16187215
     [Google Scholar]
  40. SunH. WangJ. WuH. LinS. ChenJ. WeiJ. LvS. XiongY. WeiD.Q. A multimodal deep learning framework for predicting PPI-modulator interactions.J. Chem. Inf. Model.202363237363737210.1021/acs.jcim.3c01527 38037990
     [Google Scholar]
  41. WeiY. LiW. DuT. HongZ. LinJ. Targeting HIV/HCV coinfection using a machine learning-based multiple quantitative structure-activity relationships (Multiple QSAR) method.Int. J. Mol. Sci.20192014357210.3390/ijms20143572 31336592
     [Google Scholar]
  42. KumariM. SubbaraoN. Convolutional neural network-based quantitative structure-activity relationship and fingerprint analysis against inhibitors of anthrax lethal factor.Future Med. Chem.2023151085386610.4155/fmc‑2023‑0093 37248697
     [Google Scholar]
  43. WorachartcheewanA. SongtaweeN. SiriwongS. PrachayasittikulS. NantasenamatC. PrachayasittikulV. Rational design of colchicine derivatives as anti-HIV agents via QSAR and molecular docking.Med. Chem.201915432834010.2174/1573406414666180924163756 30251609
     [Google Scholar]
  44. DengY. LiuY. TangS. ZhouC. HanX. XiaoW. Pastur-RomayL.A. Vazquez-NayaJ.M. LoureiroJ.P. MunteanuC.R. TanZ. General machine learning model, review, and experimental-theoretic study of magnolol activity in enterotoxigenic induced oxidative stress.Curr. Top. Med. Chem.2017172629772988 28828993
     [Google Scholar]
  45. Abbasi-RadmoghaddamZ. RiahiS. GharaghaniS. Mohammadi-KhanaposhtanaiM. Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies.Mol. Divers.202125126327710.1007/s11030‑020‑10063‑9 32140890
     [Google Scholar]
  46. PatrícioR.P.S. VideiraP.A. PereiraF. A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.Bioorg. Med. Chem.20225311653010.1016/j.bmc.2021.116530 34861473
     [Google Scholar]
  47. JayaprakashV. SaravananT. RavindranK. PrabhaT. SelvarajJ. JayapalanS. ChaitanyaM.V.N.L. SivakumarT. Relevance of machine learning to predict the inhibitory activity of small thiazole chemicals on estrogen receptor.Curr. Computeraided Drug Des.2023191375010.2174/1573409919666221121141646 36424784
     [Google Scholar]
  48. ShayanfarA. GhasemiS. SoltaniS. Asadpour-ZeynaliK. DoerksenR.J. JouybanA. Quantitative structure-activity relationships of imidazole-containing farnesyltransferase inhibitors using different chemometric methods.Med. Chem.20139343444810.2174/1573406411309030014 22920090
     [Google Scholar]
  49. NayarisseriA. KhandelwalR. TanwarP. MadhaviM. SharmaD. ThakurG. Speck-PlancheA. SinghS.K. Artificial Intelligence, big data and machine learning approaches in precision medicine & drug discovery.Curr. Drug Targets202122663165510.2174/18735592MTEzsMDMnz 33397265
     [Google Scholar]
  50. CarpenterK.A. CohenD.S. JarrellJ.T. HuangX. Deep learning and virtual drug screening.Future Med. Chem.201810212557256710.4155/fmc‑2018‑0314 30288997
     [Google Scholar]
  51. KumarS.A. AnandaK.T.D. BeerakaN.M. PujarG.V. SinghM. NarayanaA.H.S. BhagyalalithaM. Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry.Future Med. Chem.202214424527010.4155/fmc‑2021‑0243 34939433
     [Google Scholar]
  52. XiaoT. QiX. ChenY. JiangY. Development of ligand‐based big data deep neural network models for virtual screening of large compound libraries.Mol. Inform.20183711180003110.1002/minf.201800031 29882343
     [Google Scholar]
  53. HiroharaM. SaitoY. KodaY. SatoK. SakakibaraY. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.BMC Bioinformatics201819S1952610.1186/s12859‑018‑2523‑5 30598075
     [Google Scholar]
  54. TorngW. AltmanR.B. Graph convolutional neural networks for predicting drug-target interactions.J. Chem. Inf. Model.201959104131414910.1021/acs.jcim.9b00628 31580672
     [Google Scholar]
  55. KumariM. SubbaraoN. A hybrid resampling algorithms SMOTE and ENN based deep learning models for identification of Marburg virus inhibitors.Future Med. Chem.2022141070171510.4155/fmc‑2021‑0290 35393862
     [Google Scholar]
  56. QinT. ZhuZ. WangX.S. XiaJ. WuS. Computational representations of protein–ligand interfaces for structure-based virtual screening.Expert Opin. Drug Discov.202116101175119210.1080/17460441.2021.1929921 34011222
     [Google Scholar]
  57. Ricci-LopezJ. AguilaS.A. GilsonM.K. BrizuelaC.A. Improving structure-based virtual screening with ensemble docking and machine learning.J. Chem. Inf. Model.202161115362537610.1021/acs.jcim.1c00511 34652141
     [Google Scholar]
  58. JiangD. HsiehC.Y. WuZ. KangY. WangJ. WangE. LiaoB. ShenC. XuL. WuJ. CaoD. HouT. InteractionGraphNet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions.J. Med. Chem.20216424182091823210.1021/acs.jmedchem.1c01830 34878785
     [Google Scholar]
  59. ShimizuY. YonezawaT. SakamotoJ. FuruyaT. OsawaM. IkedaK. Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein–protein interaction-oriented library and machine learning.Sci. Rep.2021111742010.1038/s41598‑021‑86616‑1 33795749
     [Google Scholar]
  60. ZhengM. LiuZ. YanX. DingQ. GuQ. XuJ. LBVS: An online platform for ligand-based virtual screening using publicly accessible databases.Mol. Divers.201418482984010.1007/s11030‑014‑9545‑3 25182364
     [Google Scholar]
  61. BonannoE. EbejerJ.P. Applying machine learning to ultrafast shape recognition in ligand-based virtual screening.Front. Pharmacol.202010167510.3389/fphar.2019.01675 32140104
     [Google Scholar]
  62. BerrhailF. BelhadefH. HaddadM. Deep convolutional neural network to improve the performances of screening process in LBVS.Expert Syst. Appl.202220311728710.1016/j.eswa.2022.117287
     [Google Scholar]
  63. AniR. DeepaO.S. Graph convolutional neural network-based virtual screening of phytochemicals and in-silico docking studies of drug compounds for hemochromatosis.IEEE Access20231113868713869810.1109/ACCESS.2023.3338735
     [Google Scholar]
  64. SchneiderM. PonsJ.L. BourguetW. LabesseG. ElofssonA. Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity.Bioinformatics202036116016810.1093/bioinformatics/btz538 31350558
     [Google Scholar]
  65. MubashirN. FatimaR. NaeemS. Identification of novel phyto-chemicals from Ocimum basilicum for the treatment of Parkinson’s disease using in silico approach.Curr. Computeraided Drug Des.202016442043410.2174/1573409915666190503113617 32883197
     [Google Scholar]
  66. WangZ. BelecciuT. EavesJ. ReimersM. BachmannM.H. WoldringD. Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction.J. Biomol. Struct. Dyn.202341146643666310.1080/07391102.2022.2112976 35993534
     [Google Scholar]
  67. SahuA. MishraJ. KushwahaN. Artificial Intelligence (AI) in drugs and pharmaceuticals.Comb. Chem. High Throughput Screen.202225111818183710.2174/1386207325666211207153943 34875986
     [Google Scholar]
  68. 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/s11427‑018‑9342‑2 30054833
     [Google Scholar]
  69. TripathiN. GoshishtM.K. SahuS.K. AroraC. Applications of artificial intelligence to drug design and discovery in the big data era: A comprehensive review.Mol. Divers.20212531643166410.1007/s11030‑021‑10237‑z 34110579
     [Google Scholar]
  70. PiroozmandF. MohammadipanahF. SajediH. Spectrum of deep learning algorithms in drug discovery.Chem. Biol. Drug Des.202096388690110.1111/cbdd.13674 33058458
     [Google Scholar]
  71. BornJ. ManicaM. Trends in deep learning for property-driven drug design.Curr. Med. Chem.202128387862788610.2174/0929867328666210729115728 34325627
     [Google Scholar]
  72. HudsonI.L. Data integration using advances in machine learning in drug discovery and molecular biology.Artif. Neural Networ2021219016718410.1007/978‑1‑0716‑0826‑5_7
     [Google Scholar]
  73. 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]
  74. GoelM. AggarwalR. SridharanB. PalP.K. PriyakumarU.D. Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods.Wiley Interdiscip. Rev. Comput. Mol. Sci.2023132e163710.1002/wcms.1637
     [Google Scholar]
  75. MerchantJ.P. ZhuK. HenrionM.Y.R. ZaidiS.S.A. LauB. MoeinS. AlampreseM.L. PearseR.V. BennettD.A. Ertekin-TanerN. Young-PearseT.L. ChangR. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease.Commun. Biol.20236150310.1038/s42003‑023‑04791‑5 37188718
     [Google Scholar]
  76. FreyhultE. GustafssonM.G. StrömbergssonH. A machine learning approach to explain drug selectivity to soluble and membrane protein targets.Mol. Inform.2015341445210.1002/minf.201400121 27490861
     [Google Scholar]
  77. RifaiogluA.S. NalbatE. AtalayV. MartinM.J. Cetin-AtalayR. DoğanT. Deepscreen: High performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations.Chem. Sci. (Camb.)20201192531255710.1039/C9SC03414E 33209251
     [Google Scholar]
  78. ShaoK. ZhangY. WenY. ZhangZ. HeS. BoX. DTI-HETA: Prediction of drug–target interactions based on GCN and GAT on heterogeneous graph.Brief. Bioinform.2022233bbac10910.1093/bib/bbac109 35380622
     [Google Scholar]
  79. JukičM. BrenU. Machine learning in antibacterial drug design.Front. Pharmacol.20221386441210.3389/fphar.2022.864412 35592425
     [Google Scholar]
  80. JingY. BianY. HuZ. WangL. XieX.Q.S. Deep learning for drug design: An artificial intelligence paradigm for drug discovery in the big data era.AAPS J.20182035810.1208/s12248‑018‑0210‑0 29603063
     [Google Scholar]
  81. D’SouzaS. KvP. BalajiS. Training recurrent neural networks as generative neural networks for molecular structures: How does it impact drug discovery?Expert Opin. Drug Discov.202217101071107910.1080/17460441.2023.2134340 36216812
     [Google Scholar]
  82. BianY. WangJ. JunJ.J. XieX.Q. Deep Convolutional Generative Adversarial Network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors.Mol. Pharm.201916114451446010.1021/acs.molpharmaceut.9b00500 31589460
     [Google Scholar]
  83. Peña-GuerreroJ. NguewaP.A. García-SosaA.T. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases.Wiley Interdiscip. Rev. Comput. Mol. Sci.2021115e151310.1002/wcms.1513
     [Google Scholar]
  84. MouchlisV.D. AfantitisA. SerraA. FratelloM. PapadiamantisA.G. AidinisV. LynchI. GrecoD. MelagrakiG. Advances in de novo drug design: From conventional to machine learning methods.Int. J. Mol. Sci.2021224167610.3390/ijms22041676 33562347
     [Google Scholar]
  85. JiangP. ChiY. LiX.S. MengZ. LiuX. HuaX-S. XiaK. Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.Brief. Bioinform.2022231bbab52710.1093/bib/bbab527 34958660
     [Google Scholar]
  86. GaoH. ChenC. LiS. WangC. ZhouW. YuB. Prediction of protein-protein interactions based on ensemble residual convolutional neural network.Comput. Biol. Med.202315210647110.1016/j.compbiomed.2022.106471
     [Google Scholar]
  87. ClaytonE.A. PujolT.A. McDonaldJ.F. QiuP. Leveraging TCGA gene expression data to build predictive models for cancer drug response.BMC Bioinformatics202021S1436410.1186/s12859‑020‑03690‑4 32998700
     [Google Scholar]
  88. SchperbergA.V. BoichardA. TsigelnyI.F. RichardS.B. KurzrockR. Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials.Int. J. Cancer202014792537254910.1002/ijc.33240 32745254
     [Google Scholar]
  89. SharmaA. RaniR. An integrated framework for identification of effective and synergistic anti-cancer drug combinations.J. Bioinform. Comput. Biol.2018165185001710.1142/S0219720018500178 30304987
     [Google Scholar]
  90. WangB. TanX. GuoJ. XiaoT. JiaoY. ZhaoJ. WuJ. WangY. Drug-induced immune thrombocytopenia toxicity prediction based on machine learning.Pharmaceutics202214594310.3390/pharmaceutics14050943 35631529
     [Google Scholar]
  91. GongY. TengD. WangY. GuY. WuZ. LiW. TangY. LiuG. In silico prediction of potential drug‐induced nephrotoxicity with machine learning methods.J. Appl. Toxicol.202242101639165010.1002/jat.4331 35429013
     [Google Scholar]
  92. MuraliV. MuralidharY.P. KönigsC. NairM. MadhuS. NedungadiP. SrinivasaG. AthriP. Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning.Chem. Biol. Drug Des.2022100216918410.1111/cbdd.14092 35587730
     [Google Scholar]
  93. SangS. YangZ. LiuX. WangL. LinH. WangJ. DumontierM. GrEDeL: A knowledge graph embedding based method for drug discovery from biomedical literatures.IEEE Access201978404841510.1109/ACCESS.2018.2886311
     [Google Scholar]
  94. AskrH. ElgeldawiE. Aboul EllaH. ElshaierY.A.M.M. GomaaM.M. HassanienA.E. Deep learning in drug discovery: An integrative review and future challenges.Artif. Intell. Rev.20235675975603710.1007/s10462‑022‑10306‑1 36415536
     [Google Scholar]
  95. BaskinI.I. The power of deep learning to ligand-based novel drug discovery.Expert Opin. Drug Discov.202015775576410.1080/17460441.2020.1745183 32228116
     [Google Scholar]
  96. ShiT. YangY. HuangS. ChenL. KuangZ. HengY. MeiH. Molecular image-based convolutional neural network for the prediction of ADMET properties.Chemom. Intell. Lab. Syst.201919410385310.1016/j.chemolab.2019.103853
     [Google Scholar]
  97. TianQ. DingM. YangH. YueC. ZhongY. DuZ. LiuD. LiuJ. DengY. Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks.Comb. Chem. High Throughput Screen.202225463464110.2174/1386207324666210215101825 33588722
     [Google Scholar]
  98. XuX. XuanP. ZhangT. ChenB. ShengN. Inferring drug-target interactions based on random walk and convolutional neural network.IEEE/ACM Trans. Comput. Biol. Bioinformatics20221942294230410.1109/TCBB.2021.3066813 33729947
     [Google Scholar]
  99. HuS. XiaD. ChenP. WangB. Using novel convolutional neural networks architecture to predict drug-target interactions.Intelligent Computing Theories and Application.Springer201843243710.1007/978‑3‑319‑95933‑7_52
     [Google Scholar]
  100. KavipriyaG. ManjulaD. Drug–target interaction prediction model using optimal recurrent neural network.Intell. Aut Soft Comput.20233521675168910.32604/iasc.2023.027670
     [Google Scholar]
  101. WangS. DuZ. DingM. ZhaoR. Rodriguez-PatonA. SongT. LDCNN-DTI: A novel light deep convolutional neural network for drug-target interaction predictions.2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)Seoul, Korea (South)16-19 December 20201132113610.1109/BIBM49941.2020.9313585
     [Google Scholar]
  102. SonJ. KimD. KimD. Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.PLoS One2021164e024940410.1371/journal.pone.0249404 33831016
     [Google Scholar]
  103. WangX. LiuJ. ZhangC. WangS. SSGraphCPI: A novel model for predicting compound-protein interactions based on deep learning.Int. J. Mol. Sci.2022237378010.3390/ijms23073780 35409140
     [Google Scholar]
  104. WardahW. DehzangiA. TaherzadehG. RashidM.A. KhanM.G.M. TsunodaT. SharmaA. Predicting protein-peptide binding sites with a deep convolutional neural network.J. Theor. Biol.202049611027810.1016/j.jtbi.2020.110278 32298689
     [Google Scholar]
  105. YanX. LiuY. ZhangW. Deep graph and sequence representation learning for drug response prediction.Artificial Neural Networks and Machine Learning – ICANN 2022.Springer20229710810.1007/978‑3‑031‑15919‑0_9
     [Google Scholar]
  106. JiaP. HuR. PeiG. DaiY. WangY.Y. ZhaoZ. Deep generative neural network for accurate drug response imputation.Nat. Commun.2021121174010.1038/s41467‑021‑21997‑5 33741950
     [Google Scholar]
  107. ChenJ. ZhangL. A survey and systematic assessment of computational methods for drug response prediction.Brief. Bioinform.202122123224610.1093/bib/bbz164 31927568
     [Google Scholar]
  108. MingxunZ. ZhigangM. JingyiW. AhmadS. Drug response prediction based on 1d convolutional neural network and attention mechanism.Comput. Math. Methods Med.202220221610.1155/2022/8671348 36164615
     [Google Scholar]
  109. ZitnikM. AgrawalM. LeskovecJ. Modeling polypharmacy side effects with graph convolutional networks.Bioinformatics20183413i457i46610.1093/bioinformatics/bty294 29949996
     [Google Scholar]
  110. HuangW. LiC. JuY. GaoY. The next generation of machine learning in DDIs prediction.Curr. Pharm. Des.202127232728273610.2174/1381612827666210127122312 33504300
     [Google Scholar]
  111. ShimY. LeeM. KimP.J. KimH.G. A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction.BMC Bioinformatics202223116310.1186/s12859‑022‑04698‑8 35513784
     [Google Scholar]
  112. JiangH.J. YouZ.H. HuangY.A. Predicting drug−disease associations via sigmoid kernel-based convolutional neural networks.J. Transl. Med.201917138210.1186/s12967‑019‑2127‑5 31747915
     [Google Scholar]
  113. KimE. ChoiA. NamH. Drug repositioning of herbal compounds via a machine-learning approach.BMC Bioinformatics201920S1024710.1186/s12859‑019‑2811‑8 31138103
     [Google Scholar]
  114. UdrescuL. BogdanP. ChişA. SîrbuI.O. TopîrceanuA. VăruţR.M. UdrescuM. Uncovering new drug properties in target-based drug-drug similarity networks.Pharmaceutics202012987910.3390/pharmaceutics12090879 32947845
     [Google Scholar]
  115. KumarK. ChupakhinV. VosA. MorrisonD. RassokhinD. DellwoM.J. McCormickK. PaternosterE. CeulemansH. DesJarlaisR.L. Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolism and excretion properties.Future Med. Chem.202113191639165410.4155/fmc‑2021‑0138 34528444
     [Google Scholar]
  116. SiramshettyV.B. NguyenD.T. MartinezN.J. SouthallN.T. SimeonovA. ZakharovA.V. Critical assessment of artificial intelligence methods for prediction of hERG channel inhibition in the “Big Data” era.J. Chem. Inf. Model.202060126007601910.1021/acs.jcim.0c00884 33259212
     [Google Scholar]
  117. KorolevV. MitrofanovA. KorotcovA. TkachenkoV. Graph convolutional neural networks as “General-Purpose” Property Predictors: The universality and limits of applicability.J. Chem. Inf. Model.2020601222810.1021/acs.jcim.9b00587 31860296
     [Google Scholar]
  118. Hassan-HarrirouH. ZhangC. LemminT. RosENet: Improving binding affinity prediction by leveraging molecular mechanics energies with an ensemble of 3D convolutional neural networks.J. Chem. Inf. Model.20206062791280210.1021/acs.jcim.0c00075 32392050
     [Google Scholar]
  119. FernandesP.O. MartinsD.M. de Souza BozziA. MartinsJ.P.A. de MoraesA.H. MaltarolloV.G. Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.Mol. Divers.20212531301131410.1007/s11030‑021‑10261‑z 34191245
     [Google Scholar]
  120. SafizadehH. SimpkinsS.W. NelsonJ. LiS.C. PiotrowskiJ.S. YoshimuraM. YashirodaY. HiranoH. OsadaH. YoshidaM. BooneC. MyersC.L. Improving measures of chemical structural similarity using machine learning on chemical–genetic interactions.J. Chem. Inf. Model.20216194156417210.1021/acs.jcim.0c00993 34318674
     [Google Scholar]
  121. JohnL. SoujanyaY. MahantaH.J. Narahari SastryG. Chemoinformatics and machine learning approaches for identifying antiviral compounds.Mol. Inform.2022414210019010.1002/minf.202100190 34811938
     [Google Scholar]
  122. MalikA.A. OjhaS.C. SchaduangratN. NantasenamatC. ABCpred: A webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors.Mol. Divers.202226146748710.1007/s11030‑021‑10292‑6 34609711
     [Google Scholar]
  123. TrapotsiM.A. MervinL.H. AfzalA.M. SturmN. EngkvistO. BarrettI.P. BenderA. Comparison of chemical structure and cell morphology information for multitask bioactivity predictions.J. Chem. Inf. Model.20216131444145610.1021/acs.jcim.0c00864 33661004
     [Google Scholar]
  124. RodriguesC.H.M. PiresD.E.V. AscherD.B. pdCSM-PPI: Using graph-based signatures to identify protein–protein interaction inhibitors.J. Chem. Inf. Model.202161115438544510.1021/acs.jcim.1c01135 34719929
     [Google Scholar]
  125. AgyapongO. MillerW.A. WilsonM.D. KwofieS.K. Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.Mol. Divers.20222642231224210.1007/s11030‑021‑10329‑w 34626303
     [Google Scholar]
  126. RedkarS. MondalS. JosephA. HareeshaK.S. A machine learning approach for drug‐target interaction prediction using wrapper feature selection and class balancing.Mol. Inform.2020395190006210.1002/minf.201900062 32003548
     [Google Scholar]
  127. GrebnerC. MatterH. KofinkD. WenzelJ. SchmidtF. HesslerG. Application of deep neural network models in drug discovery programs.ChemMedChem202116243772378610.1002/cmdc.202100418 34596968
     [Google Scholar]
  128. ZhaoY. TianY. PangX. LiG. ShiS. YanA. Classification of FLT3 inhibitors and SAR analysis by machine learning methods.Mol. Divers.20232841995 37142889
     [Google Scholar]
  129. NazarovaA.L. YangL. LiuK. MishraA. KaliaR.K. NomuraK. NakanoA. VashishtaP. RajakP. Dielectric polymer property prediction using recurrent neural networks with optimizations.J. Chem. Inf. Model.20216152175218610.1021/acs.jcim.0c01366 33871989
     [Google Scholar]
  130. LiR. TianY. YangZ. JiY. DingJ. YanA. Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.Mol. Divers.20232731037105110.1007/s11030‑022‑10466‑w 35737257
     [Google Scholar]
  131. WarszyckiD. StruskiŁ. ŚmiejaM. KafelR. KurczabR. Pharmacoprint: A combination of a pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design.J. Chem. Inf. Model.202161105054506510.1021/acs.jcim.1c00589 34547888
     [Google Scholar]
  132. SandhuH. KumarR.N. GargP. Machine learning-based modeling to predict inhibitors of acetylcholinesterase.Mol. Divers.202226133134010.1007/s11030‑021‑10223‑5 33891263
     [Google Scholar]
  133. ZhangJ. ChenH. De Novo molecule design using molecular generative models constrained by ligand–protein interactions.J. Chem. Inf. Model.202262143291330610.1021/acs.jcim.2c00177 35793555
     [Google Scholar]
  134. HuoD. WangH. QinZ. TianY. YanA. Building 2D classification models and 3D Comsia models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.Mol. Divers.20222631715173010.1007/s11030‑021‑10300‑9 34636023
     [Google Scholar]
  135. SunH. WangY. ChenC.Z. XuM. GuoH. ItkinM. ZhengW. ShenM. Identification of SARS-CoV-2 viral entry inhibitors using machine learning and cell-based pseudotyped particle assay.Bioorg. Med. Chem.20213811611910.1016/j.bmc.2021.116119 33831697
     [Google Scholar]
  136. WangH. QinZ. YanA. Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.Mol. Divers.20212531597161610.1007/s11030‑020‑10165‑4 33534023
     [Google Scholar]
  137. ScantleburyJ. VostL. CarberyA. HadfieldT.E. TurnbullO.M. BrownN. ChenthamarakshanV. DasP. GrosjeanH. von DelftF. DeaneC.M. A small step toward generalizability: Training a machine learning scoring function for structure-based virtual screening.J. Chem. Inf. Model.202363102960297410.1021/acs.jcim.3c00322 37166179
     [Google Scholar]
  138. ShanW. LiX. YaoH. LinK. Convolutional neural network-based virtual screening.Curr. Med. Chem.202128102033204710.2174/0929867327666200526142958 32452320
     [Google Scholar]
  139. Veit-AcostaM. de AzevedoJunior W.F. The impact of crystallographic data for the development of machine learning models to predict protein-ligand binding affinity.Curr. Med. Chem.202128347006702210.2174/0929867328666210210121320 33568025
     [Google Scholar]
  140. Veit-AcostaM. de AzevedoJunior W.F. Computational prediction of binding affinity for CDK2-ligand complexes. A protein target for cancer drug discovery.Curr. Med. Chem.202229142438245510.2174/0929867328666210806105810 34365938
     [Google Scholar]
  141. Bitencourt-FerreiraG. de AzevedoJunior W.F. Electrostatic potential energy in protein-drug complexes.Curr. Med. Chem.202128244954497110.2174/1875533XMTEzhODQlw 33593246
     [Google Scholar]
  142. SundinI. VoronovA. XiaoH. PapadopoulosK. BjerrumE.J. HeinonenM. PatronovA. KaskiS. EngkvistO. Human-in-the-loop assisted de novo molecular design.J. Cheminform.20221418610.1186/s13321‑022‑00667‑8 36578043
     [Google Scholar]
  143. RaykaM. FirouziR. GB‐score: Minimally designed machine learning scoring function based on distance‐weighted interatomic contact features.Mol. Inform.2023423220013510.1002/minf.202200135 36722733
     [Google Scholar]
  144. RaykaM. Karimi-JafariM.H. FirouziR. ET‐score: Improving protein‐ligand binding affinity prediction based on distance‐weighted interatomic contact features using extremely randomized trees algorithm.Mol. Inform.2021408206008410.1002/minf.202060084 34021703
     [Google Scholar]
  145. ZamanR. NewtonM.A.H. MataeimoghadamF. SattarA. Constraint guided neighbor generation for protein structure prediction.IEEE Access202210549915500110.1109/ACCESS.2022.3176945
     [Google Scholar]
  146. KhashanR. TropshaA. ZhengW. Data mining meets machine learning: A novel ANN-based multi‐body interaction docking scoring function (MBI-score) based on utilizing frequent geometric and chemical patterns of interfacial atoms in native protein‐ligand complexes.Mol. Inform.2022418210024810.1002/minf.202100248 35142086
     [Google Scholar]
  147. BieniekM.K. CreeB. PirieR. HortonJ.T. TatumN.J. ColeD.J. An open-source molecular builder and free energy preparation workflow.Commun. Chem.20225113610.1038/s42004‑022‑00754‑9 36320862
     [Google Scholar]
  148. BjerrumE.J. MargreitterC. BlaschkeT. KolarovaS. de CastroR.L.R. Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES.J. Comput. Aided Mol. Des.202337837339410.1007/s10822‑023‑00512‑6 37329395
     [Google Scholar]
  149. WangZ. ZhengL. WangS. LinM. WangZ. KongA.W.K. MuY. WeiY. LiW. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.Brief. Bioinform.2023241bbac52010.1093/bib/bbac520 36502369
     [Google Scholar]
  150. ZhangX. ShenC. WangT. KangY. LiD. PanP. WangJ. WangG. DengY. XuL. CaoD. HouT. WangZ. Topology-based and conformation-based decoys database: An unbiased online database for training and benchmarking machine-learning scoring functions.J. Med. Chem.202366139174918310.1021/acs.jmedchem.3c00801 37317043
     [Google Scholar]
  151. WójcikowskiM. BallesterP.J. SiedleckiP. Performance of machine-learning scoring functions in structure-based virtual screening.Sci. Rep.2017714671010.1038/srep46710 28440302
     [Google Scholar]
  152. Tran-NguyenV.K. SimeonS. JunaidM. BallesterP.J. Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions.Curr. Res. Struct. Biol.2022420621010.1016/j.crstbi.2022.06.002 35769111
     [Google Scholar]
  153. MarivaniI. TsiligianniE. CornelisB. DeligiannisN. Designing CNNs for multimodal image restoration and fusion via unfolding the method of multipliers.IEEE Trans. Circ. Syst. Video Tech.20223295830584510.1109/TCSVT.2022.3163649
     [Google Scholar]
  154. RazzaghiP. AbbasiK. ShiraziM. RashidiS. Multimodal brain tumor detection using multimodal deep transfer learning.Appl. Soft Comput.202212910963110.1016/j.asoc.2022.109631
     [Google Scholar]
  155. YuzhakovaD.V. LermontovaS.A. GrigoryevI.S. MuravievaM.S. GavrinaA.I. ShirmanovaM.V. BalalaevaI.V. KlapshinaL.G. ZagaynovaE.V. In vivo multimodal tumor imaging and photodynamic therapy with novel theranostic agents based on the porphyrazine framework-chelated gadolinium (III) cation.Biochim. Biophys. Acta, Gen. Subj.20171861123120313010.1016/j.bbagen.2017.09.004 28916141
     [Google Scholar]
  156. RaceA.M. SuttonD. HammG. MaglennonG. MortonJ.P. StrittmatterN. CampbellA. SansomO.J. WangY. BarryS.T. TakátsZ. GoodwinR.J.A. BunchJ. Deep learning-based annotation transfer between molecular imaging modalities: An automated workflow for multimodal data integration.Anal. Chem.20219363061307110.1021/acs.analchem.0c02726 33534548
     [Google Scholar]
  157. CaoL. XuZ. ShangT. ZhangC. WuX. WuY. ZhaiS. ZhanZ. DuanH. Multi_CycGT: A deep learning-based multimodal model for predicting the membrane permeability of cyclic peptides.J. Med. Chem.20246731888189910.1021/acs.jmedchem.3c01611 38270541
     [Google Scholar]
  158. ZhengS. LeiZ. AiH. ChenH. DengD. YangY. Deep scaffold hopping with multimodal transformer neural networks.J. Cheminform.20211318710.1186/s13321‑021‑00565‑5 34774103
     [Google Scholar]
  159. PradhanT. GuptaO. ChawlaG. The future of chatGPT in medicinal chemistry: Harnessing AI for accelerated drug discovery.ChemistrySelect2024913e20230435910.1002/slct.202304359
     [Google Scholar]
  160. JinsongS. QifengJ. XingC. HaoY. WangL. Molecular fragmentation as a crucial step in the AI-based drug development pathway.Commun. Chem.2024712010.1038/s42004‑024‑01109‑2 38302655
     [Google Scholar]
  161. TelentiA. AuliM. HieB.L. MaherC. SariaS. IoannidisJ.P.A. Large language models for science and medicine.Eur. J. Clin. Invest.2024546e1418310.1111/eci.14183 38381530
     [Google Scholar]
  162. WuZ. ChenJ. LiY. DengY. ZhaoH. HsiehC.Y. HouT. From black boxes to actionable insights: A perspective on explainable artificial intelligence for scientific discovery.J. Chem. Inf. Model.202363247617762710.1021/acs.jcim.3c01642 38079566
     [Google Scholar]
  163. MahjourB. HoffstadtJ. CernakT. Designing chemical reaction arrays using phactor and ChatGPT.Org. Process Res. Dev.20232781510151610.1021/acs.oprd.3c00186
     [Google Scholar]
  164. DimitriadisF. AlkagietS. TsigkrikiL. KleitsiotiP. SidiropoulosG. EfstratiouD. AskalidiT. TsaousidisA. SiarkosM. GiannakopoulouP. MavrogianniA.D. ZarifisJ. KoulaouzidisG. ChatGPT and patients with heart failure.Angiology20240003319724123840310.1177/00033197241238403 38451243
     [Google Scholar]
  165. GalidoP.V. ButalaS. ChakerianM. AgustinesD. A case study demonstrating applications of chatgpt in the clinical management of treatment-resistant schizophrenia.Cureus2023156e3816610.7759/cureus.38166
     [Google Scholar]
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The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis
Curr. Neuropharmacol. 24, 74 (2026); https://doi.org/10.2174/011570159X384988250430093924
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  • Article Type:     Research Article
Keyword(s): Artificial intelligence; bibliometrics; deep learning; drug discovery; machine learning; medicinal chemistry

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