Author(s):
Alok Kumar Upadhyay, Nisha Kumari, Nidhi Gupta, Sachin Kumar
Email(s):
alokupadhyay741@gmail.com
DOI:
10.52711/0975-4377.2025.00016
Address:
Alok Kumar Upadhyay1*, Nisha Kumari2, Nidhi Gupta3, Sachin Kumar4
1,2,4Institute of Technology and Management, 274301, Uttar Pradesh, India.
3ITM Collage of Pharmacy and Research, 274301, Uttar Pradesh, India.
*Corresponding Author
Published In:
Volume - 17,
Issue - 2,
Year - 2025
ABSTRACT:
The combination of artificial intelligence (AI) and medication research has resulted in a dramatic shift in the pharmaceutical sector. AI-driven technologies, including as machine learning (ML), deep learning (DL), and natural language processing (NLP), are rapidly being used at all stages of drug discovery, from target identification to clinical trials. This paper delves into current advances in AI applications throughout the drug development pipeline, demonstrating how AI models are improving predictive accuracy, optimizing compound screening, and expediting lead identification. We investigate the use of AI in repurposing current medications, discovering biomarkers for personalized medicine, and enhancing clinical trial design by predicting patient responses and optimizing dosing regimes. Furthermore, we describe how multi-omics data, AI-driven simulation models, and automated high-throughput screening technologies are accelerating the usually lengthy and expensive drug discovery process. Despite AI's promise, obstacles persist in data quality, model interpretability, and regulatory hurdles. The review concludes by outlining future directions for AI in drug development, emphasizing the importance of interdisciplinary collaboration and the potential for AI to revolutionize the way drugs are discovered and brought to market, offering new hope for precision medicine and the treatment of complex diseases.
Cite this article:
Alok Kumar Upadhyay, Nisha Kumari, Nidhi Gupta, Sachin Kumar. AI Convergence in Drug Development and Recent Applications: A Review. Research Journal of Pharmaceutical Dosage Forms and Technology. 2025; 17(2):107-4. doi: 10.52711/0975-4377.2025.00016
Cite(Electronic):
Alok Kumar Upadhyay, Nisha Kumari, Nidhi Gupta, Sachin Kumar. AI Convergence in Drug Development and Recent Applications: A Review. Research Journal of Pharmaceutical Dosage Forms and Technology. 2025; 17(2):107-4. doi: 10.52711/0975-4377.2025.00016 Available on: https://rjpdft.com/AbstractView.aspx?PID=2025-17-2-4
REFERENCE:
1. Raparthi M, Gayam R.S, Nimmagadda S.P.V, Sahu K.M, Putha S, Pattyam P.S, Kondapaka K.K, Kasaraneni P.B, Thuniki P, Kuna S.S. AI Assisted Drug Discovery: Emphasizing Its Role in Accelerating Precision Medicine Initiatives and Improving Treatment Outcomes. Human-Computer Interaction Perspectives. 2022; 2(2): 1-10.
2. Visan L.A, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life. 2024; 14(233): 2-36.
3. Kaul V, Enslin S, Gross S.A. History of artificial intelligence in medicine. Gastrointest. Endosc. 2020; 92: 807–812.
4. Wojtara M, Rana E, Rahman T, Khanna P, Singh H. Artificial intelligence in rare disease diagnosis and treatment. Artificial Intelligence in Rare Diseases. 2023; 16: 2106–2111.
5. Huang B, Huang H, Zhang S et al. Artificial intelligence in pancreatic cancer. Theranostics. 2022;12(16):6931-6954.
6. Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second- generation artificial intelligence- based system for improving diagnosis, treatment, and monitoring of patients with rare dis eases. Eur J Hum Genet. 2021; 29(10): 1485-1490.
7. Faviez C, Chen X, Garcelon N, et al. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis. 2020; 15(1): 94.
8. DeepChem. Available online: https://github.com/deepchem/deepchem (accessed on 20 October 2023).
9. Bhatt T. K, Nimesh, S. The Design and Development of Novel Drugs and Vaccines: Principles and Protocols; Academic Press: Cambridge, MA, USA, 2021; ISBN 978-0-12-821475-6.
10. Mathai N, Chen Y, Kirchmair J. Validation strategies for target prediction methods. Brief. Bioinform. 2020;21: 791–802.
11. Rifaioglu A.S, Atas H, Martin M.J, Cetin-Atalay R, Atalay V, Dogan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Brief. Bioinform. 2019;20 :1878–1912.
12. Zhang O, Zhang J, Jin J, Zhang X, Hu R, Shen C, Cao H, Du H, Kang Y, Deng Y et al. ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat. Mach. Intell. 2023; 5:1020–1030.
13. Paulz D, Sanapz D, Shenoyz S, Kalyane D, Kalia K, Tekade K.R. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1): 80-92.
14. Zang, Q. et al. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J. Chem. Inf. Model. 2017;57: 36–49.
15. Yang, X. et al. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 2019; 119: 10520–10594.
16. Hessler G, Baringhaus K.H. Artificial intelligence in drug design. Molecules. 2018;23: 2520.
17. O ¨ztu ¨rk, H. et al. DeepDTA: deep drug–target binding affinity prediction. Bioinformatics. 2018;34: 821–829.
18. Das P.K, Chandra J. Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges; Frontiers in medical technology. 2023;3-14.
19. Sacha G.M, Varona P. Artificial Intelligence in Nanotechnology. Nanotechnology. 2013; 24(45):452002.
20. Priyom Bose. Optimizing Drug Delivery Using AI [Internet]. News Medical.net. (2022) [cited 2022 Oct 5] Available from: https:// www.azolifesciences. Com /article /Optimizing-Drug-Deliv ery-UsingAI.aspx
21. Colombo S. Chapter 4– applications of artificial intelligence in drug delivery and pharmaceutical development. In: A Bohr, K Memarzadeh, editors. Artificial intelligence in healthcare. London, UK: Academic Press (2020). p. 85–116. Available at: https://www.sciencedirect.com/science/article/pii/ B9780128184387000046 (cited October 9, 2022).
22. Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharmaceutics. 2016; 13(7):2524–2530.
23. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: an advanced generative adversarial autoencoder model for de Novo generation of new molecules with desired molecular properties in silico. Mol Pharmaceutics.2017 ;14(9):3098–3104.
24. Menden M.P, Iorio F, Garnett M, McDermott U, Benes C.H, Ballester PJ, et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS One.2013 ;8(4): e61318.
25. Pivetta T, Isaia F, Trudu F, Pani A, Manca M, Perra D, et al. Development and validation of a general approach to predict and quantify the synergism of anticancer drugs using experimental design and artificial neural networks. Talanta. 2013; 115:84–93.
26. Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G. Deepsynergy: predicting anticancer drug synergy with deep learning. Bioinformatics. 2018 ;34(9):1538–46.
27. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol.2020; 60: 573–589.
28. Ciallella H.L. and Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol. 2019 ;32; 536–547.
29. Chan H.S. et al. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 2019;40 (8): 592–604.
30. Brown N. Silico Medicinal Chemistry: Computational Methods to Support Drug Design. Royal Society of Chemistry 2015.
31. Pereira J.C. et al. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016 ;56: 2495–2506.
32. Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice; Springer Nature. 2022;35: 23-32.
33. Aeffner F. et al. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J. Pathol. Inf. 2019;10: 9.
34. Heindl A, Nawaz S, Yuan, Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Invest. 2015 ;95: 377–384.
35. Yuan J. et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J. Immunother. Cancer 2016 ;4: 3.
36. Aeffner F. et al. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J. Pathol. Inf.2019; 10: 9
37. Bera K, Schalper K. A, Rimm D. L, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology– new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol.2019; 16: 703–715.
38. Vamathevan J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 2019;18: 463–477.
39. Serag A. et al. Translational AI and deep learning in diagnostic pathology. Front. Med.2019; 6: 185.
40. Barsoum I, Tawedrous E, Faragalla H, Yousef G. M. Histo-genomics: digital pathology at the forefront of precision medicine. Diagnosis. 2019;6: 203–212.
41. Pantanowitz L. et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch. Pathol. Lab. Med. 2013 ;137: 1710–1722.
42. Zarella, M. D. et al. A practical guide to whole slide imaging: a white paper from the Digital Pathology Association. Arch. Pathol. Lab. Med. 2019;143: 222–234.
43. Bera K, Schalper K. A, Rimm D. L, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology– new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol.2019; 16: 703–715.
44. Tumeh P. C et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol. 2017; 5: 417–424.
45. Barisoni L, Lafata K. J, Hewitt S. M, Madabhushi A, Balis U. G. J. Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 2020; 16: 669–685.
46. Neltner J. H et al. Digital pathology and image analysis for robust high throughput quantitative assessment of Alzheimer disease neuropathologic changes. J. Neuropathol. Exp. Neurol. 2012 ;71: 1075–1085.
47. Visibelli A, Roncaglia B, Spiga O, Santucci A. The impact of ar tificial intelligence in the odyssey of rare diseases. Biomedicine. 2023;11(3):887.
48. Schlemmer HP, Bittencourt LK, D’Anastasi M, Domingues R, Khong PL, Lockhat Z, et al. Global challenges for cancer imaging. JGO. 2018; 4:1–10.
49. Niu G, Chen X. The role of molecular imaging in drug delivery. Drug Deliv (Lond). 2009 ;3: 109–13.
50. Serrano R. D, Luciano C. F, Anaya J. B, Molina G, Ongoren B, Kara A. et al. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024; 16: 1328.
51. Hassanzadeh P. et al. The significance of artificial intelligence in drug delivery system design. Adv. Drug Delivery Rev. 2019;151: 169–190.
52. Luo M et al. Micro-/nanorobots at work in active drug delivery. Adv. Funct. Mater. 2018;28.
53. Fu J, Yan H. Controlled drug release by a nanorobot. Nat. Biotechnol.2012;30: 407–408.
54. Sheu Y. H, Magdamo C, Miller M, Das S, Blacker D, Smoller, J.W. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit. Med. 2023;6: 73.
55. Arnold P.I.M, Janzing J.G.E, Hommersom A. Machine learning for antidepressant treatment selection in depression. Drug Discov. Today 2024:29.
56. Liu X, Read S.J. Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors. Front. Psychiatry 2024, 15, 1349576.
57. Walsh D, Serrano D.R, Worku Z.A, Madi A.M, O’Connell P, Twamley B et al. Engineering of pharmaceutical cocrystals in an excipient matrix: Spray drying versus hot melt extrusion. Int. J. Pharm. 2018;551: 241–256.
58. Lamy B, Tewes F, Serrano D.R, Lamarche I, Gobin P, Couet W et al. Marchand, S. New aerosol formulation to control ciprofloxacin pulmonary concentration. J. Control. Release 2018; 271: 118–126.
59. Egorov E, Pieters C, Rechtman K. H, Shklover J, Schroeder A. Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems. Drug Delivery and Translational Research. 2021;345-352.
60. Fleming N. How artificial intelligence is changing drug discovery. Nature 2018; 557: S55–S57.
61. DiNuzzo, M. How artificial intelligence enables modelling and simulation of biological networks to accelerate drug discovery. Front. Drug Discovery. 2022;2: 1019706.