Skip to content
2000
image of Technological Evolution and Hotspot Identification for Applying Artificial Intelligence in Pharmacy Based on Topic Modeling and Knowledge Graph

Abstract

Introduction

The current pharmaceutical industry has increasingly adopted artificial intelligence (AI), integrating it across the entire industrial chain. While AI improves efficiency and reduces costs, it also faces challenges. This study explores both the technological evolution and contemporary innovation hotspots of AI in pharmacy.

Methods

This study adopts a fusion analysis of multi-source data, constructing a bi-dimensional analytical framework based on patented inventions (1990-2024) and research articles (2020-2024) as research objects. The study applies the Latent Dirichlet Allocation (LDA) topic model to analyze the evolution of patent topics and employs CiteSpace to construct keyword knowledge graphs from research articles. By integrating patent and article data to define technical labels, the study identifies research hotspots from the perspective of the pharmaceutical life cycle, enabling cross-validation from both scientific and technical dimensions.

Results

The number of AI-related patents in the pharmaceutical field has grown rapidly over the past five years. Technological topics exhibit a distinct evolutionary trend. Research hotspots span the entire pharmaceutical life cycle, from drug development to clinical delivery. Additionally, potential directions for future technological development have been identified.

Discussion

Research hotspots in the application of AI in pharmaceuticals include target identification, virtual screening, drug delivery, clinical trials, and pharmacovigilance. Precision medicine and explainable AI (XAI)-driven pharmacy modeling are expected to emerge as key directions for future technological development.

Conclusion

AI has already reshaped the pharmaceutical industry through applications across all stages of the pharmaceutical life cycle. It is poised to attract growing research attention and drive innovative applications in the years ahead.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpb/10.2174/0113892010412180250930230046
2025-10-20
2025-12-14

  • From This Site

    /content/journals/cpb/10.2174/0113892010412180250930230046
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Fanni S.C. Febi M. Aghakhanyan G. Neri E. Natural Language Processing. In:Introduction to Artificial Intelligence. Klontzas M.E. Fanni S.C. Neri E. Cham Springer International Publishing 2023 87 99 10.1007/978‑3‑031‑25928‑9_5
    [Google Scholar]
  2. Kakani V. Nguyen V.H. Kumar B.P. Kim H. Pasupuleti V.R. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2020 2 100033 10.1016/j.jafr.2020.100033
    [Google Scholar]
  3. Van Roy V. Vertesy D. Damioli G. AI and Robotics Innovation. In: Handbook of Labor, Human Resources and Population Economics. Zimmermann K.F. Cham Springer International Publishing 2020 1 35 10.1007/978‑3‑319‑57365‑6_12‑2
    [Google Scholar]
  4. Turing A.M.I. —Computing machinery and Intelligence. Mind 1950 LIX 236 433 460 10.1093/mind/LIX.236.433
    [Google Scholar]
  5. Moor J. The dartmouth college Artificial Intelligence conference: The next fifty years. AI Mag. 2006 27 4 87 91
    [Google Scholar]
  6. Selmy H.A. Mohamed H.K. Medhat W. Big data analytics deep learning techniques and applications: A survey. Inf. Syst. 2024 120 102318 10.1016/j.is.2023.102318
    [Google Scholar]
  7. Lugano G. Virtual assistants and self-driving cars. 2017 15th International Conference on ITS Telecommunications (ITST) Warsaw,Poland 29-31 May 2017 1 5 10.1109/ITST.2017.7972192
    [Google Scholar]
  8. de Barcelos Silva A. Gomes M.M. da Costa C.A. da Rosa Righi R. Barbosa J.L.V. Pessin G. De Doncker G. Federizzi G. Intelligent personal assistants: A systematic literature review. Expert Syst. Appl. 2020 147 113193 10.1016/j.eswa.2020.113193
    [Google Scholar]
  9. Chen X. Xie H. Li Z. Cheng G. Leng M. Wang F.L. Information fusion and artificial intelligence for smart healthcare: A bibliometric study. Inf. Process. Manage. 2023 60 1 103113 10.1016/j.ipm.2022.103113
    [Google Scholar]
  10. Gharbaoui O.E. Kiyadi I. Boukhari H.E. Evaluating AI and ML in network security: A comprehensive literature review. Procedia Comput. Sci. 2024 251 727 733 10.1016/j.procs.2024.11.176
    [Google Scholar]
  11. Rashid A.B. Kausik M.D.A.K. AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances 2024 7 100277 10.1016/j.hybadv.2024.100277
    [Google Scholar]
  12. Samin O.B. Algeelani N.A.A. Bathich A. Omar M. Mansoor M. Khan A. Optimizing agricultural data security: Harnessing IoT and AI with Latency Aware Accuracy Index (LAAI). PeerJ Comput. Sci. 2024 10 2276 10.7717/peerj‑cs.2276 39314708
    [Google Scholar]
  13. Kerdvibulvech C. Big data and AI-driven evidence analysis: A global perspective on citation trends, accessibility, and future research in legal applications. J. Big Data 2024 11 1 180 10.1186/s40537‑024‑01046‑w
    [Google Scholar]
  14. Huanbutta K. Burapapadh K. Kraisit P. Sriamornsak P. Ganokratanaa T. Suwanpitak K. Sangnim T. Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. Eur. J. Pharm. Sci. 2024 203 106938 10.1016/j.ejps.2024.106938 39419129
    [Google Scholar]
  15. Liu J. Wang X. Ye X. Chen D. Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management. Front. Nutr. 2025 11 1481073 10.3389/fnut.2024.1481073 39839291
    [Google Scholar]
  16. Rajpurkar P. Chen E. Banerjee O. Topol E.J. AI in health and medicine. Nat. Med. 2022 28 1 31 38 10.1038/s41591‑021‑01614‑0 35058619
    [Google Scholar]
  17. Mak K.K. Pichika M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today 2019 24 3 773 780 10.1016/j.drudis.2018.11.014 30472429
    [Google Scholar]
  18. Suriyaamporn P. Pamornpathomkul B. Patrojanasophon P. Ngawhirunpat T. Rojanarata T. Opanasopit P. The Artificial Intelligence-powered new era in pharmaceutical research and development: A review. AAPS PharmSciTech 2024 25 6 188 10.1208/s12249‑024‑02901‑y 39147952
    [Google Scholar]
  19. Wang T. Wu M.B. Lin J.P. Yang L.R. Quantitative structure–activity relationship: Promising advances in drug discovery platforms. Expert Opin. Drug Discov. 2015 10 12 1283 1300 10.1517/17460441.2015.1083006 26358617
    [Google Scholar]
  20. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 2020 60 573 589 10.1146/annurev‑pharmtox‑010919‑023324 31518513
    [Google Scholar]
  21. Wallach I. Dzamba M. Heifets A. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv 2015 10.48550/arXiv.1510.02855
    [Google Scholar]
  22. Sanchez-Lengeling B. Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 2018 361 6400 360 365 10.1126/science.aat2663 30049875
    [Google Scholar]
  23. Feng X. Ma Z. Yu C. Xin R. MRNDR: Multihead attention-based recommendation network for drug repurposing. J. Chem. Inf. Model. 2024 64 7 2654 2669 10.1021/acs.jcim.3c01726 38373300
    [Google Scholar]
  24. Zhao X. Liu T. He Y.N. Fang W. Li X. Jiang W. Comparison of the efficacy and safety of low-dose antihypertensive combinations in patients with hypertension: Protocol for a systematic review and network meta-analysis. BMJ Open 2024 14 10 086323 10.1136/bmjopen‑2024‑086323 39448211
    [Google Scholar]
  25. Singh S. Kumar R. Payra S. Singh S.K. Artificial Intelligence and Machine Learning in pharmacological research: Bridging the gap between data and drug discovery. Cureus 2023 15 8 44359 10.7759/cureus.44359 37779744
    [Google Scholar]
  26. Blei D.M. Latent Dirichlet allocation. J. Mach. Learn. Res. 2003 3 993 1022 10.7551/mitpress/1120.003.0082
    [Google Scholar]
  27. Guang Z. Gaozhi P. Fengjing L. The topic evolution of information privacy from the perspective of temporal correlation and structural representation. Inf. Sci. 2022 40 4 127 137 10.13833/j.issn.1007‑7634.2022.04.016
    [Google Scholar]
  28. Chen C. Searching for intellectual turning points: Progressive knowledge domain visualization Proc Natl Acad. Sci. USA, 2004 101 Suppl 1 5303 5310 Suppl 1 10.1073/pnas.0307513100 14724295
    [Google Scholar]
  29. Chen C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2006 57 3 359 377 10.1002/asi.20317
    [Google Scholar]
  30. Zhong D. Li Y. Huang Y. Hong X. Li J. Jin R. Molecular mechanisms of exercise on cancer: A bibliometrics study and visualization analysis via citespace. Front. Mol. Biosci. 2022 8 797902 10.3389/fmolb.2021.797902 35096970
    [Google Scholar]
  31. Zhang F. Wang L. Zhao J. Zhang X. Medical applications of generative adversarial network: A visualization analysis. Acta Radiol. 2023 64 10 2757 2767 10.1177/02841851231189035 37603577
    [Google Scholar]
  32. Zhang Y. Yu C. Zhao F. Xu H. Zhu C. Li Y. Landscape of Artificial Intelligence in breast cancer (2000–2021): A bibliometric analysis. Front. Biosci. 2022 27 8 224 10.31083/j.fbl2708224 36042161
    [Google Scholar]
  33. Dhar M. Bhattacharya P. Comparison of the logistic and the Gompertz curve under different constraints. J. Stat. Manag. Syst. 2018 21 7 1189 1210 10.1080/09720510.2018.1488414
    [Google Scholar]
  34. Hu R. Cai T. Xu W. Exploring the technology changes of new energy vehicles in China: Evolution and trends. Comput. Ind. Eng. 2024 191 110178 10.1016/j.cie.2024.110178
    [Google Scholar]
  35. Han W. Han X. Zhou S. Zhu Q. The development history and research tendency of medical informatics: Topic evolution analysis. JMIR Med. Inform. 2022 10 1 31918 10.2196/31918 35084351
    [Google Scholar]
  36. Feng J. Study on the method of identification and analysis of research fronts. Dissertataion. Jilin University 2017
    [Google Scholar]
  37. Pun F.W. Ozerov I.V. Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacol. Sci. 2023 44 9 561 572 10.1016/j.tips.2023.06.010 37479540
    [Google Scholar]
  38. Chen R. Liu X. Jin S. Lin J. Liu J. Machine Learning for drug-target interaction prediction. Molecules 2018 23 9 2208 10.3390/molecules23092208 30200333
    [Google Scholar]
  39. Kovalevskiy O. Mateos-Garcia J. Tunyasuvunakool K. AlphaFold two years on: Validation and impact. Proc. Natl. Acad. Sci. USA 2024 121 34 2315002121 10.1073/pnas.2315002121 39133843
    [Google Scholar]
  40. Song J. Xu Z. Cao L. Wang M. Hou Y. Li K. The discovery of new drug-target interactions for breast cancer treatment. Molecules 2021 26 24 7474 10.3390/molecules26247474 34946556
    [Google Scholar]
  41. Neves B.J. Braga R.C. Melo-Filho C.C. Moreira-Filho J.T. Muratov E.N. Andrade C.H. QSAR-based virtual screening: Advances and applications in drug discovery. Front. Pharmacol. 2018 9 1275 10.3389/fphar.2018.01275 30524275
    [Google Scholar]
  42. Giordano D. Biancaniello C. Argenio M.A. Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals 2022 15 5 646 10.3390/ph15050646 35631472
    [Google Scholar]
  43. Aundhia C. Parmar G. Talele C. Shah N. Talele D. Impact of Artificial Intelligence on drug development and delivery. Curr. Top. Med. Chem. 2024 10.2174/0115680266324522240725053634 39136506
    [Google Scholar]
  44. Serrano D.R. Luciano F.C. Anaya B.J. Ongoren B. Kara A. Molina G. Ramirez B.I. Sánchez-Guirales S.A. Simon J.A. Tomietto G. Rapti C. Ruiz H.K. Rawat S. Kumar D. Lalatsa A. Artificial Intelligence (AI) Applications in drug discovery and drug delivery: Revolutionizing personalized medicine. Pharmaceutics 2024 16 10 1328 10.3390/pharmaceutics16101328 39458657
    [Google Scholar]
  45. Gholap A.D. Uddin M.J. Faiyazuddin M. Omri A. Gowri S. Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput. Biol. Med. 2024 178 108702 10.1016/j.compbiomed.2024.108702 38878397
    [Google Scholar]
  46. Smietana K. Siatkowski M. Møller M. Trends in clinical success rates. Nat. Rev. Drug Discov. 2016 15 6 379 380 10.1038/nrd.2016.85 27199245
    [Google Scholar]
  47. Chaudhari N. Ravi R. Gogtay N.J. Thatte U.M. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect. Clin. Res. 2020 11 2 64 69 10.4103/picr.PICR_206_19 32670830
    [Google Scholar]
  48. Matsui D. Strategies to measure and improve patient adherence in clinical trials. Pharmaceut. Med. 2009 23 5-6 289 297 10.1007/BF03256784
    [Google Scholar]
  49. Zhu Z. Roy D. Feng S. Vogler B. AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders. Schizophr. Res. 2025 275 42 51 10.1016/j.schres.2024.11.006 39637767
    [Google Scholar]
  50. Liang L. Hu J. Sun G. Hong N. Wu G. He Y. Li Y. Hao T. Liu L. Gong M. Artificial Intelligence-based pharmacovigilance in the setting of limited resources. Drug Saf. 2022 45 5 511 519 10.1007/s40264‑022‑01170‑7 35579814
    [Google Scholar]
  51. Rajkomar A. Oren E. Chen K. Dai A.M. Hajaj N. Hardt M. Liu P.J. Liu X. Marcus J. Sun M. Sundberg P. Yee H. Zhang K. Zhang Y. Flores G. Duggan G.E. Irvine J. Le Q. Litsch K. Mossin A. Tansuwan J. Wang D. Wexler J. Wilson J. Ludwig D. Volchenboum S.L. Chou K. Pearson M. Madabushi S. Shah N.H. Butte A.J. Howell M.D. Cui C. Corrado G.S. Dean J. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 2018 1 1 18 10.1038/s41746‑018‑0029‑1 31304302
    [Google Scholar]
  52. Dong C. Ji Y. Fu Z. Qi Y. Yi T. Yang Y. Sun Y. Sun H. Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover. iScience 2025 28 3 112044 10.1016/j.isci.2025.112044 40104052
    [Google Scholar]
  53. Pratap A. Hamada M. Screening of key transcripts from expression data using applied Artificial Intelligence for cancer prediction. Int. J. Comput. Intell. Syst. 2024 17 1 252 10.1007/s44196‑024‑00657‑8
    [Google Scholar]
  54. Xu X. Jia Q. Yuan H. Qiu H. Dong Y. Xie W. Yao Z. Zhang J. Nie Z. Li X. Shi Y. Zou J.Y. Huang M. Zhuang J. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med. Image Anal. 2023 90 102953 10.1016/j.media.2023.102953 37734140
    [Google Scholar]
  55. Li H. Xia C. Wang T. Wang Z. Cui P. Li X. GRASS: Learning spatial–temporal properties from chainlike cascade data for microscopic diffusion prediction. IEEE Trans. Neural Netw. Learn. Syst. 2024 35 11 16313 16327 10.1109/TNNLS.2023.3293689
    [Google Scholar]
  56. Ponzoni I. Páez Prosper J.A. Campillo N.E. Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2023 13 6 1681 10.1002/wcms.1681
    [Google Scholar]
/content/journals/cpb/10.2174/0113892010412180250930230046
Loading
Technological Evolution and Hotspot Identification for Applying Artificial Intelligence in Pharmacy Based on Topic Modeling and Knowledge Graph
Bentham Science Publishers ; https://doi.org/10.2174/0113892010412180250930230046
/content/journals/cpb/10.2174/0113892010412180250930230046
/content/journals/cpb/10.2174/0113892010412180250930230046
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: patents ; pharmaceutical life cycle ; Artificial intelligence ; knowledge graph ; topic evolution ; pharmacy ; topic modeling

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