Trends in research on AI-aided drug discovery from 2009 to 2023: A 15-year bibliometric analysis

Abstract

Purpose: In recent years, the rapid advancement of artificial intelligence technology has brought opportunities for the acceleration and improvement of the drug discovery process by aiding in all stages of drug discovery like drug target identification and validation, virtual screening, de novo drug design, and ADMET property prediction. The present study aims to provide an overview of the developing tendency, cooperation, and influence of academic groups and individuals, hotspots, and crucial problems in the field of AI-aided drug discovery using bibliometric methods.

Methods: Publications on AI-aided drug discovery published from January 1, 2009, to December 31, 2023, were retrieved from the Web of Science core collection. The document type was limited to articles or reviews, and the language was set to English. Citespace was used to conduct the bibliometric analysis.

Results: A total of 9700 publications were included, and the number of them generally increased over time, with a rapid increase tendency since 2018. The US and China were the leading countries in this field. The Chinese Academy of Sciences was the most influential institution. Ekins, Sean was the most productive author and Hou, Tingjun formed the largest cooperation network. Networks and clusters of keywords highlighted terms like “virtual screening”, “expression” and “drug delivery” as focused topics, and burst analysis showed that “support vector machines”, and “classification” received the longest attention. Meanwhile the keywords “sars cov 2”, “molecular design” and “clinical trials” were hotspots in recent years. The content analysis of the co-cited literature identified the significant questions to be tackled in future research.

Conclusions: This study offers a comprehensive landscape of the global contributions given to this increasingly important and prolific field of research and points out several areas that might be addressed by future research to better develop the field of AI-aided drug discovery.

Keywords

Bibliometric analysis / Artificial intelligence / Drug discovery

References

[1] Hamet P , Tremblay J . Artificial intelligence in medicine. Metab, Clin Exp. 2017; 69: S36- S40.

[2] Tran BX , Vu GT , Ha GH , et al. Global evolution of research in artificial intelligence in Health and medicine: a bibliometric study. J Clin Med. 2019; 8 (3).

[3] Polishchuk PG , Madzhidov TI , Varnek A . Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des. 2013; 27 (8): 675- 679.

[4] Dimasi JA , Grabowski HG , Hansen RW . Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016; 47: 20- 33.

[5] Paul D , Sanap G , Shenoy S , et al. Artificial intelligence in drug discovery and development. Drug Discov Today. 2020; 26 (1): 80- 93.

[6] Lo YC , Rensi SE , Torng W , et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018; 23 (8): 1538- 1546.

[7] Huang SJ , Cai NG , Pacheco PP , et al. Applications of support vector machine (SVM) learning in cancer genomics. CANCER GENOMICS PROTEOMICS. 2018; 15 (1): 41- 51.

[8] Hu P , Zou J , Yu J , et al. De novo drug design based on Stack-RNN with multiobjective reward-weighted sum and reinforcement learning. J Mol Model. 2023; 29 (4): 121.

[9] Shan W , Li X , Yao H , et al. Convolutional neural network-based virtual screening. Curr Med Chem. 2021; 28 (10): 2033- 2047.

[10] BenevolentAI Announces First Patient Dosed in its Atopic Dermatitis Clinical Trial_BenevolentAI (AMS_ Bai)[EB/OL].

[11] Ninkov A , Frank JR , Maggio LA . Bibliometrics: methods for studying academic publishing. Perspect Med Educ. 2022; 11 (3): 173- 176.

[12] Chen C . Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci U S A. 2004; 101 (Suppl 1): 5303- 5310. Suppl 1.

[13] Jiang S , Liu Y , Zheng H , et al. Evolutionary patterns and research frontiers in neoadjuvant immunotherapy: a bibliometric analysis. Int J Surg. 2023; 109 (9): 2774- 2783.

[14] Wei N , Xu Y , Li Y , et al. A bibliometric analysis of T cell and atherosclerosis. Front Immunol. 2022; 13: 948314.

[15] Wang H , Shi J , Shi S , et al. Bibliometric analysis on the progress of chronic heart failure. Curr Probl Cardiol. 2022; 47 (9): 101213.

[16] Li X , Su Z , Wang C , et al. Mapping the evolution of inhaled drug delivery research: trends, collaborations, and emerging frontiers. Drug Discov Today. 2024; 29 (2): 103864.

[17] Wei B , Huang H , Cao Q , et al. Bibliometric and visualized analysis of the applications of exosomes based drug delivery. Biomed Pharmacother. 2024; 176: 116803.

[18] Zhang J , Shahbaz M , Ijaz M , et al. Bibliometric analysis of antimalarial drug resistance. Front Cell Infect Microbiol. 2024; 14: 1270060.

[19] Shrestha S , Danekhu K , Kc B , et al. Bibliometric analysis of adverse drug reactions and pharmacovigilance research activities in Nepal. Ther Adv Drug Saf. 2020; 11: 585464432.

[20] He B , Guo J , Tong H , et al. Artificial intelligence in drug discovery: a bibliometric analysis and literature review. Mini Rev Med Chem. 2024; 24 (14): 1353- 1367.

[21] Karger E , Kureljusic M . Using artificial intelligence for drug discovery: a bibliometric study and future research agenda. Pharmaceuticals. 2022; 15 (12).

[22] Mitchell M . The Turing Test and our shifting conceptions of intelligence. Science. 2024; 385 (6710): eadq9356.

[23] Howard J . Artificial intelligence: implications for the future of work. Am J Ind Med. 2019; 62 (11): 917- 926.

[24] Raza MA , Aziz S , Noreen M , et al. Artificial intelligence (AI) in pharmacy: an overview of innovations. Innov Pharm. 2022; 13 (2).

[25] Macpherson T , Churchland A , Sejnowski T , et al. Natural and Artificial Intelligence: a brief introduction to the interplay between AI and neuroscience research. Neural Network. 2021; 144: 603- 613.

[26] van Gerven M . Computational foundations of natural intelligence. Front Comput Neurosci. 2017; 11: 112.

[27] Gawehn E , Hiss JA , Schneider G . Deep learning in drug discovery. MOLECULAR INFORMATICS. 2016; 35 (1): 3- 14.

[28] Lecun Y , Bengio Y , Hinton G . Deep learning. Nature. 2015; 521 (7553): 436- 444.

[29] Chen HM , Engkvist O , Wang YH , et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018; 23 (6): 1241- 1250.

[30] Xavier B . Advancements and future directions in polycythemia vera research: a bibliometric analysis. Curēus (Palo Alto, CA). 2024; 16 (6): e61774.

[31] Pattnaik D , Ray S , Hassan MK . Microfinance: a bibliometric exploration of the knowledge landscape. Heliyon. 2024; 10 (10): e31216.

[32] Lai B , Jiang H , Gao Y , et al. Research trends and hotspots of myositis ossificans: a bibliometric analysis from 1993 to 2022. EFORT Open Rev. 2024; 9 (7): 589- 599.

[33] K RA , Abraham KSJ , Jose J , et al. Navigating the Web of influence: a bibliometric analysis of social media addiction. Curēus (Palo Alto, CA). 2024; 16 (6): e62283.

[34] Jangid H , Garg S , Kashyap P , et al. Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation. Front Microbiol. 2024; 15: 1379602.

[35] Urbina F , Lentzos F , Invernizzi C , et al. AI in drug discovery: a wake-up call. Drug Discov Today. 2023; 28 (1): 103410.

[36] Urbina F , Lentzos F , Invernizzi C , et al. Dual use of artificial intelligence-powered drug discovery. Nat Mach Intell. 2022; 4 (3): 189- 191.

[37] Chen KP , Xu RL , Hu XP , et al. Recent advances in the development of DprE1 inhibitors using AI/CADD approaches. Drug Discov Today. 2024; 29 (6).

[38] Gu SK , Yang YW , Zhao YH , et al. Evaluation of AlphaFold2 structures for hit identification across multiple scenarios. J Chem Inf Model. 2024; 64 (9): 3630- 3639.

[39] Zhang XJ , Shen C , Zhang HT , et al. Advancing ligand docking through deep learning: challenges and prospects in virtual screening. Accounts Chem Res. 2024; 57 (10): 1500- 1509.

[40] Wu H , Wang Y , Tong L , et al. Global research trends of ferroptosis: a rapidly evolving field with enormous potential. Front Cell Dev Biol. 2021; 9: 646311.

[41] Wu MQ , Wu DQ , Hu CP , et al. Studies on children with developmental coordination disorder in the past 20 Years: a bibliometric analysis via CiteSpace. Front Psychiatr. 2021; 12: 776883.

[42] Vamathevan J , Clark D , Czodrowski P , et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019; 18 (6): 463- 477.

[43] Chen H , Engkvist O , Wang Y , et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018; 23 (6): 1241- 1250.

[44] Wishart DS , Feunang YD , Guo AC , et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018; 46 (D1): D1074- D1082.

[45] Gaulton A , Hersey A , Nowotka M , et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017; 45 (D1): D945- D954.

[46] Mendez D , Gaulton A , Bento AP , et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019; 47 (D1): D930- D940.

[47] Gómez-Bombarelli R , Wei JN , Duvenaud D , et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018; 4 (2): 268- 276.

[48] Segler M , Kogej T , Tyrchan C , et al. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci. 2018; 4 (1): 120- 131.

[49] Jumper J , Evans R , Pritzel A , et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596 (7873): 583- 589.

[50] Wu Z , Ramsundar B , Feinberg EN , et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018; 9 (2): 513- 530.

[51] Öztürk H , Özgür A , Ozkirimli E . DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018; 34 (17): i821- i829.

[52] Zhavoronkov A , Ivanenkov YA , Aliper A , et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019; 37 (9): 1038- 1040.

[53] Popova M , Isayev O , Tropsha A . Deep reinforcement learning for de novo drug design. Sci Adv. 2018; 4 (7): eaap7885.

[54] Ragoza M , Hochuli J , Idrobo E , et al. Protein-ligand scoring with convolutional neural networks. J Chem Inf Model. 2017; 57 (4): 942- 957.

[55] Olivecrona M , Blaschke T , Engkvist O , et al. Molecular de-novo design through deep reinforcement learning. J Cheminf. 2017; 9 (1): 48.

[56] Yang K , Swanson K , Jin W , et al. Analyzing learned molecular representations for property prediction. J Chem Inf Model. 2019; 59 (8): 3370- 3388.

[57] Schmidt B , Hildebrandt A . From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today. 2024; 29 (6): 103990.

[58] Cerchia C , Lavecchia A . New avenues in artificial-intelligence-assisted drug discovery. Drug Discov Today. 2023; 28 (4): 103516.

[59] Kim S , Chen J , Cheng T , et al. PubChem 2023 update. Nucleic Acids Res. 2023; 51 (D1): D1373- D1380.

[60] Wang Y , Xiao J , Suzek TO , et al. PubChem's BioAssay database. Nucleic Acids Res. 2012; 40 (Database issue): D400- D412.

[61] Southan C . Caveat usor: assessing differences between major chemistry databases. ChemMedChem. 2018; 13 (6): 470- 481.

[62] Arul MN , Ruba PG , Narahari SG , et al. Artificial intelligence in virtual screening: models versus experiments. Drug Discov Today. 2022; 27 (7): 1913- 1923.

[63] Rouillard AD , Gundersen GW , Fernandez NF , et al. The harmonize: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION. 2016; 2016: baw100.

[64] Vora LK , Gholap AD , Jetha K , et al. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023; 15 (7).

[65] Siramshetty VB , Xu X , Shah P . Artificial intelligence in ADME property prediction. Methods Mol Biol. 2024; 2714: 307- 327.

RIGHTS & PERMISSIONS

The Authors. Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi Communications Co. Ltd.