THE AI REVOLUTION IN PHARMACEUTICALS: INNOVATIONS, CHALLENGES, AND FUTURE PROSPECTS – AN OVERVIEW
DOI:
https://doi.org/10.22159/ijap.2026v18i1.55744Keywords:
Artificial intelligence, Clinical trials, Drug discovery, Formulation, Machine learning, PharmaceuticalsAbstract
Artificial intelligence (AI) is transforming pharmaceutical research and development (R&D), and making measurable improvements in efficiency, precision, and cost-effectiveness in drug research and development. AI-enabled platforms have cut the drug discovery pipeline timelines in comparison to the traditional 4-6 years down to 46 days, along with speeding up compound screening by 1-2 years and reduced clinical trial duration by up to 59% and increased the accuracy of patient selection 80-90%. In formulation optimization artificial neural networks, neuro--fuzzy systems, and hybrid model-based AI models have been able to predict dissolution profile and critical quality attributes with accuracy rates of over 90%, with 30-50% lower experimental workload. In this review, the cross-domain evidence on the use of AI in the continuum of target identification to regulatory integration is thoroughly synthesized and critical evaluations on existing limitations which include data bias, interpretability discrepancy and regulatory ambiguity discussed. It proposes a systematized framework of integration, which places the emphasis on creating high impact pilot projects, in- the-wild testing and further monitoring or observing of models according to the instructions of FDA, EMA and EU AI Act. Synthesizing measures of quantitative values along with practical measures, the present work offers a blueprint of unambiguously converting the ideological potential of AI into implementable, regulator-compatible utilities in pharmaceutical science.
References
1. Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, et al. AI in drug discovery and its clinical relevance. Heliyon [Internet]. 2023 July 1 [cited 2025 Mar 13];9(7). Available from: https://www.cell.com/heliyon/abstract/S2405-8440(23)04783-7
2. Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, et al. A review on machine learning approaches and trends in drug discovery. Computational and Structural Biotechnology Journal. 2021 Jan 1;19:4538–58.
3. Serrano DR, Luciano FC, Anaya BJ, Ongoren B, Kara A, Molina G, et al. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024 Oct;16(10):1328.
4. Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther. 2023;17:2691–725.
5. Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023 July;15(7):1916.
6. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019 Mar;24(3):773–80.
7. Murray JD, Lange JJ, Bennett-Lenane H, Holm R, Kuentz M, O’Dwyer PJ, et al. Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation. European Journal of Pharmaceutical Sciences. 2023 Dec 1;191:106562.
8. Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol. 2017 Dec 1;7(4):351–67.
9. Kumar D, Kumar P, Ahmed I, Singh S. INTEGRATING ARTIFICIAL INTELLIGENCE IN DISEASE DIAGNOSIS, TREATMENT, AND FORMULATION DEVELOPMENT: A REVIEW. Asian Journal of Pharmaceutical and Clinical Research. 2023 Nov 7;1–8.
10. Younis HA, Eisa TAE, Nasser M, Sahib TM, Noor AA, Alyasiri OM, et al. A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges. Diagnostics. 2024 Jan;14(1):109.
11. Chang AC. Big data in medicine: The upcoming artificial intelligence. Progress in Pediatric Cardiology. 2016 Dec;43:91–4.
12. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, et al. Detecting novel associations in large data sets. Science. 2011 Dec 16;334(6062):1518–24.
13. Smalley E. AI-powered drug discovery captures pharma interest. Nat Biotechnol. 2017 July;35(7):604–5.
14. Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021 Dec 1;7(4):432–9.
15. Mishra S, Bhatt T, Kumar H, Jain R, Shilpi S, Jain V. Nanoconstructs for theranostic application in cancer: Challenges and strategies to enhance the delivery. Front Pharmacol [Internet]. 2023 Mar 15 [cited 2025 Mar 13];14. Available from: https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1101320/full
16. Dey H, Arya N, Mathur H, Chatterjee N, Jadon R. Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design. ijnrph. 2024 June 30;30–41.
17. Mishra DV. Artificial Intelligence: The Beginning of a New Era in Pharmacy Profession. Asian Journal of Pharmaceutics (AJP) [Internet]. 2018 May 30 [cited 2025 Mar 13];12(02). Available from: https://www.asiapharmaceutics.info/index.php/ajp/article/view/2317
18. Gao M, Liu S, Chen J, Gordon KC, Tian F, McGoverin CM. Potential of Raman spectroscopy in facilitating pharmaceutical formulations development – An AI perspective. International Journal of Pharmaceutics. 2021 Mar;597:120334.
19. Landin M, Rowe RC. Artificial neural networks technology to model, understand, and optimize drug formulations. In: Formulation Tools for Pharmaceutical Development [Internet]. Elsevier; 2013 [cited 2025 Mar 13]. p. 7–37. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9781907568992500025
20. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine. 2023 June;13(6):951.
21. Ali KA, Mohin S, Mondal P, Goswami S, Ghosh S, Choudhuri S. Influence of artificial intelligence in modern pharmaceutical formulation and drug development. Futur J Pharm Sci. 2024 Mar 29;10(1):53.
22. Patel V, Shah M. Artificial intelligence and machine learning in drug discovery and development. Intelligent Medicine. 2022 Aug 1;2(3):134–40.
23. T S, T C, Marpaka S, K S. ASPECTS OF UTILIZATION AND LIMITATIONS OF ARTIFICIAL INTELLIGENCE IN DRUG SAFETY. Asian Journal of Pharmaceutical and Clinical Research. 2021 Aug 7;34–9.
24. Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, Amir E, et al. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 2025 Mar 14;13(1):45.
25. AbdelSalam FM. Blockchain Revolutionizing Healthcare Industry: A Systematic Review of Blockchain Technology Benefits and Threats. Perspect Health Inf Manag. 2023 Sept 1;20(3):1b.
26. FDA DSCA blockchain interpolability - Google Search [Internet]. [cited 2025 July 30]. Available from: https://www.google.com/search?q=FDA+DSCA+blockchain+interpolability&rlz=1C1VDKB_enIN1105IN1105&oq=FDA+DSCA+blockchain+interpolability&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIICAEQABgWGB4yDQgCEAAYhgMYgAQYigUyDQgDEAAYhgMYgAQYigUyBwgEEAAY7wUyBwgFEAAY7wUyBwgGEAAY7wXSAQkxMjIwM2owajSoAgCwAgE&sourceid=chrome&ie=UTF-8
27. Meshram SI, Hatwar PR, Bakal RL, Raut PV. Artificial Intelligence-Assisted Fabrication of 3D Printed Technology in Pharmaceutical Development and Its Application. Journal of Drug Delivery and Therapeutics. 2024 Aug 15;14(8):214–22.
28. Göller AH, Kuhnke L, Montanari F, Bonin A, Schneckener S, ter Laak A, et al. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discovery Today. 2020 Sept 1;25(9):1702–9.
29. Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC, et al. Machine Learning in Drug Discovery and Development Part 1: A Primer. CPT: Pharmacometrics & Systems Pharmacology. 2020;9(3):129–42.
30. Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artif Intell Rev. 2022 Mar 1;55(3):1947–99.
31. Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules. 2020 Nov 12;25(22):5277.
32. Damarla R. Enhancement of drug discovery with machine learning clustering algorithms. Journal of High School Science [Internet]. 2022 May 14 [cited 2025 Mar 14];6(2). Available from: https://jhss.scholasticahq.com/article/35568-enhancement-of-drug-discovery-with-machine-learning-clustering-algorithms
33. Madugula SS, John L, Nagamani S, Gaur AS, Poroikov VV, Sastry GN. Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing. Computers in Biology and Medicine. 2021 Nov 1;138:104856.
34. Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discovery Today. 2015 Mar 1;20(3):318–31.
35. Heikamp K, Bajorath J. Support vector machines for drug discovery. Expert Opinion on Drug Discovery. 2014 Jan;9(1):93–104.
36. Rodríguez-Pérez R, Bajorath J. Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J Comput Aided Mol Des. 2022 May 1;36(5):355–62.
37. Wassermann AM, Geppert H, Bajorath J. Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors. J Chem Inf Model. 2009 Mar 23;49(3):582–92.
38. Wassermann AM, Geppert H, Bajorath J. Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects. J Chem Inf Model. 2009 Oct;49(10):2155–67.
39. Podolyan Y, Walters MA, Karypis G. Assessing Synthetic Accessibility of Chemical Compounds Using Machine Learning Methods. J Chem Inf Model. 2010 June 28;50(6):979–91.
40. Lind P, Maltseva T. Support vector machines for the estimation of aqueous solubility. J Chem Inf Comput Sci. 2003;43(6):1855–9.
41. Deelder W, Napier G, Campino S, Palla L, Phelan J, Clark TG. A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis. BMC Genomics. 2022 Jan 11;23(1):46.
42. Bai LY, Dai H, Xu Q, Junaid M, Peng SL, Zhu X, et al. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm. International Journal of Molecular Sciences. 2018 Feb;19(2):467.
43. Lowe R, Mussa HY, Nigsch F, Glen RC, Mitchell JB. Predicting the mechanism of phospholipidosis. J Cheminform. 2012 Jan 26;4:2.
44. Nigsch F, Bender A, Jenkins JL, Mitchell JBO. Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model. 2008 Dec;48(12):2313–25.
45. Arian R, Hariri A, Mehridehnavi A, Fassihi A, Ghasemi F. Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm. Computational Biology and Chemistry. 2020 June 1;86:107269.
46. Konovalov DA, Coomans D, Deconinck E, Heyden YV. Benchmarking of QSAR models for blood-brain barrier permeation. J Chem Inf Model. 2007;47(4):1648–56.
47. Votano JR, Parham M, Hall LH, Kier LB, Oloff S, Tropsha A, et al. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis. 2004 Sept;19(5):365–77.
48. Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev. 2023 July 1;56(7):5975–6037.
49. Lavecchia A. Advancing drug discovery with deep attention neural networks. Drug Discovery Today. 2024 Aug 1;29(8):104067.
50. Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular Therapy - Nucleic Acids. 2023 Mar 14;31:691–702.
51. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discovery Today. 2018 June 1;23(6):1241–50.
52. Goh GB, Siegel C, Vishnu A, Hodas N, Baker N. How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) [Internet]. 2018 [cited 2025 Mar 15]. p. 1340–9. Available from: https://ieeexplore.ieee.org/abstract/document/8354255
53. Puentes PR, Valderrama N, González C, Daza L, Muñoz-Camargo C, Cruz JC, et al. PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. PLOS ONE. 2021 Apr 26;16(4):e0241728.
54. Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, et al. Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. Mol Pharmaceutics. 2018 Oct 1;15(10):4398–405.
55. Martinelli DD. Generative machine learning for de novo drug discovery: A systematic review. Computers in Biology and Medicine. 2022 June 1;145:105403.
56. Zeng X, Xiang H, Yu L, Wang J, Li K, Nussinov R, et al. Accurate prediction of molecular targets using a self-supervised image representation learning framework. Res Sq. 2022 Apr 7;rs.3.rs-1477870.
57. Alsenan S, Al-Turaiki I, Hafez A. A Recurrent Neural Network model to predict blood–brain barrier permeability. Computational Biology and Chemistry. 2020 Dec 1;89:107377.
58. Wei B, Zhang Y, Gong X. DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing. Sci Rep. 2022 Oct 28;12(1):18200.
59. Ochiai T, Inukai T, Akiyama M, Furui K, Ohue M, Matsumori N, et al. Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity. Commun Chem. 2023 Nov 16;6(1):249.
60. Macedo B, Ribeiro Vaz I, Taveira Gomes T. MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design. Sci Rep. 2024 Jan 12;14(1):1212.
61. Dong J, Wu Z, Xu H, Ouyang D. FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence. Briefings in Bioinformatics. 2024 Jan 1;25(1):bbad419.
62. Jelsch M, Roggo Y, Kleinebudde P, Krumme M. Model predictive control in pharmaceutical continuous manufacturing: A review from a user’s perspective. Eur J Pharm Biopharm. 2021 Feb;159:137–42.
63. Aksu B, Paradkar A, de Matas M, Ozer O, Güneri T, York P. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech. 2012 Dec;13(4):1138–46.
64. Mahdi WA, Alhowyan A, Obaidullah AJ. Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations. Sci Rep. 2025 July 11;15(1):25139.
65. Galata DL, Könyves Z, Nagy B, Novák M, Mészáros LA, Szabó E, et al. Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. International Journal of Pharmaceutics. 2021 Mar 15;597:120338.
66. Das PJ, Preuss C, Mazumder B. Chapter 13 - Artificial Neural Network as Helping Tool for Drug Formulation and Drug Administration Strategies. In: Puri M, Pathak Y, Sutariya VK, Tipparaju S, Moreno W, editors. Artificial Neural Network for Drug Design, Delivery and Disposition [Internet]. Boston: Academic Press; 2016 [cited 2025 Mar 20]. p. 263–76. Available from: https://www.sciencedirect.com/science/article/pii/B9780128015599000132
67. Landín M, Rowe RC, York P. Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations. European Journal of Pharmaceutical Sciences. 2009 Nov 5;38(4):325–31.
68. Cleophas EPEM, Cleophas TJ. Fuzzy modeling, a novel approach to studying pharmacodynamics. Am J Ther. 2014;21(1):20–5.
69. Singh I, Kaur J, Kaur S, Barik BR, Pahwa R. Artificial Neural Networks and Neuro-Fuzzy Models: Applications in Pharmaceutical Product Development. Braz arch biol technol. 2023 July 3;66:e23210769.
70. Rebouh S, Lefnaoui S, Bouhedda M, Yahoum MM, Hanini S. Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives. Drug Deliv Transl Res. 2019 Feb;9(1):162–77.
71. Mesut B, Aksu N, Ozsoy Y. Design of Sustained Release Tablet Formulations of Alfuzosin HCl by means of Neuro-Fuzzy Logic. LATIN AMERICAN JOURNAL OF PHARMACY [Internet]. 2013 [cited 2025 July 30];32. Available from: https://avesis.istanbul.edu.tr/yayin/f1e4b53a-2164-4815-8874-b15ea4bab88b/design-of-sustained-release-tablet-formulations-of-alfuzosin-hcl-by-means-of-neuro-fuzzy-logic
72. Tan C, Degim İT. Development of sustained release formulation of an antithrombotic drug and application of Fuzzy logic. Pharm Dev Technol. 2012;17(2):242–50.
73. Fatouros DG, Nielsen FS, Douroumis D, Hadjileontiadis LJ, Mullertz A. In vitro–in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks. European Journal of Pharmaceutics and Biopharmaceutics. 2008 Aug 1;69(3):887–98.
74. AlAlaween WH, Mahfouf M, Salman AD. When swarm meets fuzzy logic: Batch optimisation for the production of pharmaceuticals. Powder Technology. 2021 Feb 1;379:174–83.
75. Azadeh A, Neshat N, Kazemi A, Saberi M. Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach. Int J Adv Manuf Technol. 2012 Jan 1;58(5):585–96.
76. Takayama K, Morva A, Fujikawa M, Hattori Y, Obata Y, Nagai T. Formula optimization of theophylline controlled-release tablet based on artificial neural networks. J Control Release. 2000 Aug 10;68(2):175–86.
77. Ibrić S, Jovanović M, Djurić Z, Parojcić J, Solomun L. The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit RS PO as matrix substance. J Control Release. 2002 Aug 21;82(2–3):213–22.
78. Plumb AP, Rowe RC, York P, Doherty C. The effect of experimental design on the modeling of a tablet coating formulation using artificial neural networks. Eur J Pharm Sci. 2002 Sept;16(4–5):281–8.
79. Leane MM, Cumming I, Corrigan OI. The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets. AAPS PharmSciTech. 2003;4(2):E26.
80. Agatonovic-Kustrin S, Alany RG. Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems. Pharm Res. 2001 July;18(7):1049–55.
81. Barmpalexis P, Grypioti A, Eleftheriadis GK, Fatouros DG. Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming. AAPS PharmSciTech. 2018 Feb;19(2):741–52.
82. Turkoglu M, Aydin I, Murray M, Sakr A. Modeling of a roller-compaction process using neural networks and genetic algorithms. European Journal of Pharmaceutics and Biopharmaceutics. 1999 Nov 1;48(3):239–45.
83. Barmpalexis P, Kachrimanis K, Georgarakis E. Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming. Eur J Pharm Biopharm. 2011 Jan;77(1):122–31.
84. Barmpalexis P, Kanaze FI, Kachrimanis K, Georgarakis E. Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation. European Journal of Pharmaceutics and Biopharmaceutics. 2010 Feb 1;74(2):316–23.
85. Examination of Clinical Trial Costs and Barriers for Drug Development [Internet]. ASPE. 2014 [cited 2025 Mar 24]. Available from: http://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0
86. Askin S, Burkhalter D, Calado G, El Dakrouni S. Askin, Scott, Denis Burkhalter, Gilda Calado, and Samar El Dakrouni. “Artificial Intelligence Applied to Clinical Trials: Opportunities and Challenges.” Health and Technology 13, no. 2 (March 1, 2023): 203–13. https://doi.org/10.1007/s12553-023-00738-2. Health Technol. 2023 Mar 1;13(2):203–13.
87. Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends Pharmacol Sci. 2019 Aug;40(8):577–91.
88. Lee CS, Lee AY. How Artificial Intelligence Can Transform Randomized Controlled Trials. Transl Vis Sci Technol. 9(2):9.
89. Mourya A, Jobanputra B, Pai R. AI-powered clinical trials and the imperative for regulatory transparency and accountability. Health Technol. 2024 Nov 1;14(6):1071–81.
90. Agency EM. EMA qualifies first artificial intelligence tool to diagnose inflammatory liver disease (MASH) in biopsy samples | European Medicines Agency (EMA) [Internet]. 2025 [cited 2025 Mar 24]. Available from: https://www.ema.europa.eu/en/news/ema-qualifies-first-artificial-intelligence-tool-diagnose-inflammatory-liver-disease-mash-biopsy-samples
91. Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021 Aug 16;22(1):537.
92. Zame WR, Bica I, Shen C, Curth A, Lee HS, Bailey S, et al. Machine learning for clinical trials in the era of COVID-19. Stat Biopharm Res. 12(4):506–17.
93. Krittanawong C, Johnson KW, Tang WW. How artificial intelligence could redefine clinical trials in cardiovascular medicine: lessons learned from oncology. Per Med. 2019 Mar;16(2):83–8.
94. Sangari N, Qu Y. A Comparative Study on Machine Learning Algorithms for Predicting Breast Cancer Prognosis in Improving Clinical Trials. In: 2020 International Conference on Computational Science and Computational Intelligence (CSCI) [Internet]. 2020 [cited 2025 Mar 24]. p. 813–8. Available from: https://ieeexplore.ieee.org/document/9457981
95. Kolla L, Gruber FK, Khalid O, Hill C, Parikh RB. The case for AI-driven cancer clinical trials - The efficacy arm in silico. Biochim Biophys Acta Rev Cancer. 2021 Aug;1876(1):188572.
96. Vazquez J, Abdelrahman S, Byrne LM, Russell M, Harris P, Facelli JC. Using supervised machine learning classifiers to estimate likelihood of participating in clinical trials of a de-identified version of ResearchMatch. J Clin Transl Sci. 2020 Sept 4;5(1):e42.
97. Wang L, Song Y, Wang H, Zhang X, Wang M, He J, et al. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel). 2023 Feb 7;16(2):253.
98. Gazali, Kaur S, Singh I. Artificial intelligence based clinical data management systems: A review. Informatics in Medicine Unlocked. 2017 Jan 1;9:219–29.
99. Smith JA, Abhari RE, Hussain Z, Heneghan C, Collins GS, Carr AJ. Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study. BMJ Open. 2020 Oct 14;10(10):e039969.
100. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019 June;18(6):463–77.
101. Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 15(10):e46454.
102. Fu C, Chen Q. The future of pharmaceuticals: Artificial intelligence in drug discovery and development. Journal of Pharmaceutical Analysis. 2025 Feb 26;101248.
103. Fu L, Jia G, Liu Z, Pang X, Cui Y. The applications and advances of artificial intelligence in drug regulation: A global perspective. Acta Pharmaceutica Sinica B. 2025 Jan 1;15(1):1–14.
104. Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, et al. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. 2023 June;16(6):891.
105. Paranjape K, Schinkel M, Hammer RD, Schouten B, Nannan Panday RS, Elbers PWG, et al. The Value of Artificial Intelligence in Laboratory Medicine. Am J Clin Pathol. 2021 May 18;155(6):823–31.
106. Singh RP, Hom GL, Abramoff MD, Campbell JP, Chiang MF, on behalf of the AAO Task Force on Artificial Intelligence. Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient. Translational Vision Science & Technology. 2020 Aug 11;9(2):45.
107. Bettanti A, Beccari AR, Biccarino M. Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges? Discov Artif Intell. 2024 Dec 5;4(1):102.
108. Blatter TU, Witte H, Nakas CT, Leichtle AB. Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics. 2022 Aug;12(8):1923.
109. Alizadehsani R, Oyelere SS, Hussain S, Jagatheesaperumal SK, Calixto RR, Rahouti M, et al. Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey. IEEE Access. 2024;12:35796–812.
110. Polzer A, Fleiß J, Ebner T, Kainz P, Koeth C, Thalmann S. Validation of AI-based Information Systems for Sensitive Use Cases: Using an XAI Approach in Pharmaceutical Engineering. In 2022 [cited 2025 Mar 6]. Available from: http://hdl.handle.net/10125/79518
111. Viganò EL, Ballabio D, Roncaglioni A. Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks. Toxics. 2024 Jan;12(1):87.
112. Cao Y, Romero J, Aspuru-Guzik A. Potential of quantum computing for drug discovery. IBM Journal of Research and Development. 2018 Nov;62(6):6:1-6:20.
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