Volume : 13, Issue : 03, March – 2026

Title:

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PHARMACEUTICAL RESEARCH: TRANSFORMING DRUG DISCOVERY, FORMULATION DEVELOPMENT, AND QUALITY ASSURANCE

Authors :

Snehal Sunil Patil, Vishwjit Kisan Rathod, Nagane Abhijeet Ramesh, Mujawar suhana salim*, Dr. Rahul ishwara jadhav

Abstract :

Artificial intelligence (AI) and machine learning (ML) are revolutionizing pharmaceutical research by transforming conventional empirical methodologies into predictive, data-driven frameworks. Across drug discovery, AI facilitates rapid target identification through genomic and proteomic data mining, accelerates virtual screening and lead optimization, enables de novo molecular design using generative models, and improves protein structure prediction through advanced deep learning systems such as AlphaFold developed by DeepMind. In formulation development, AI enhances preformulation prediction, excipient compatibility assessment, dissolution modeling, and design space optimization under Quality by Design principles. Integration of ML with process analytical technology (PAT), digital twins, and predictive maintenance systems strengthens manufacturing efficiency and real-time quality assurance. Furthermore, AI-driven ADMET prediction and drug repurposing strategies reduce attrition rates and development costs while supporting personalized medicine initiatives.
Despite these advancements, challenges related to data quality, model interpretability, regulatory validation, and ethical governance persist. Addressing these limitations through standardized validation frameworks and explainable AI approaches is essential for sustainable implementation. Overall, AI represents a paradigm shift in pharmaceutical sciences, offering accelerated development timelines, cost efficiency, and enhanced therapeutic precision.
Keywords:
Artificial Intelligence; Machine Learning; Deep Learning; Drug Discovery; De Novo Drug Design; AlphaFold; ADMET Prediction; Formulation Optimization; Process Analytical Technology; Digital Twins; Quality Assurance; Precision Medicine

Cite This Article:

Please cite this article in press Mujawar suhana salim et al., Artificial Intelligence And Machine Learning In Pharmaceutical Research: Transforming Drug Discovery, Formulation Development, And Quality Assurance ., Indo Am. J. P. Sci, 2026; 13(02).

REFERENCES:

1. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589.
2. Tunyasuvunakool K, Adler J, Wu Z, et al. Highly accurate protein structure prediction for the human proteome. Nature. 2021;596:590-596.
3. Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871-876.
4. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2020;38(9):1038-1040.
5. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463-477.
6. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2020;25(2):409-417.
7. Walters WP, Barzilay R. Applications of deep learning in molecule generation and properties prediction. J Chem Inf Model. 2020;60(1):143-152.
8. Merk D, Friedrich L, Grisoni F, Schneider G. De novo design of bioactive small molecules by artificial intelligence. Mol Inform. 2020;39(12):e2000065.
9. Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2021;7(40):eabj5449.
10. Zhou Y, Wang F, Tang J, Nussinov R, Cheng F. Artificial intelligence in COVID-19 drug repurposing. Brief Bioinform. 2020;21(6):1634-1651.
11. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery. Nat Mater. 2020;19:435-441.
12. Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2020;19:709-710.
13. Kolluri S, Lin J, Liu R, Zhang Y, Zhang W. Machine learning and AI in pharmaceutical R&D. AAPS J. 2022;24:154.
14. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev. 2019;119:10520-10594.
15. Brown N, Ertl P, Lewis R, Luksch T, Reker D, Schneider G. Artificial intelligence in chemistry. Chem Rev. 2020;120:10854-10905.
16. Gao K, Nguyen DD, Chen J, et al. Generative network complex for drug discovery. Nat Mach Intell. 2020;2:337-346.
17. Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2020;12:33.
18. Walters WP, Murcko MA. Assessing the impact of generative AI on medicinal chemistry. J Med Chem. 2020;63:865-878.
19. Jiménez J, Skalic M, Martinez-Rosell G, De Fabritiis G. KDEEP: Protein–ligand absolute binding affinity prediction via 3D CNN. J Chem Inf Model. 2020;60:2069-2080.
20. Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P. Development and evaluation of deep learning model for protein–ligand binding affinity. Bioinformatics. 2020;36:3666-3674.
21. Li X, Fourches D. Inductive transfer learning for molecular activity prediction. J Cheminform. 2020;12:27.
22. Rifaioglu AS, et al. Deep learning for drug–target interaction prediction. Brief Bioinform. 2021;22:258-270.
23. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180:688-702.
24. Rasve V, Chakraborty AK, Jain SK, Vengurlekar S. Study of phytochemical profiling and in vitro antioxidant properties of ethanolic extract of Clematis triloba. Eur Chem Bull. 2022;11(12):2658–2677. doi:10.53555/ecb/2022.11.12.2162022.
25. Rasve VR, Paithankar VV, Shirsat MK, Dhobale AV. Evaluation of antiulcer activity of Aconitum heterophyllum in experimental animals. World J Pharm Pharm Sci. 2018;7(2):819–839.
26. Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 2020;72:44-84.
27. Huang Z, et al. Machine learning and AI in PK-PD modeling. CPT Pharmacometrics Syst Pharmacol. 2024;13:345-359.
28. Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: ADMET prediction. Drug Discov Today. 2021;26:1536-1548.
29. Wu Z, Ramsundar B, Feinberg EN, et al. MoleculeNet: Benchmark for molecular ML. Chem Sci. 2020;11:10636-10647.
30. Altae-Tran H, Ramsundar B, Pappu AS, Pande V. Low-data drug discovery with deep learning. ACS Cent Sci. 2020;6:1933-1943.
31. Huanbutta K, et al. Artificial intelligence-driven pharmaceutical industry: review. Pharmaceutics. 2024;16:456.
32. Rehman AU, et al. Role of artificial intelligence in revolutionizing drug discovery. Comput Struct Biotechnol J. 2025;23:1012-1031.
33. Fan N, et al. Machine learning approaches for ADMET prediction. Expert Opin Drug Metab Toxicol. 2025;21:245-260.
34. Venkataraman M, et al. Leveraging ML models in ADMET evaluation. Pharmaceutics. 2025;17:1220.
35. Baek M, et al. RoseTTAFold: accurate prediction of protein complexes. Science. 2024;376:adl2528.
36. Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using deep learning potentials. Nature. 2020;577:706-710.
37. FDA. Artificial Intelligence in Drug Manufacturing Discussion Paper. U.S. Food and Drug Administration; 2023.
38. EMA. Reflection paper on AI in medicinal product lifecycle. European Medicines Agency; 2024.
39. Ribeiro AG, et al. Automated visual inspection in pharmaceutical industry using AI. Sensors (Basel). 2025;25:1234.
40. Vijayakumar A, et al. Real-time visual intelligence for defect detection. Sci Rep. 2024;14:69701.
41. Rasve V, Chakraborty AK, Jain SK, Vengurlekar S. Comparative evaluation of antidiabetic activity of ethanolic leaves extract of Clematis triloba and its SMEDDS formulation in streptozotocin-induced diabetic rats. J Popul Ther Clin Pharmacol. 2022;29(4):959–971. doi:10.53555/jptcp.v29i04.2360
42. Pathak KA, et al. Deep learning-based defect detection in tablets. Int J Pharm. 2025;650:123-135.
43. Diószegi A, et al. Tablet defect prediction using CNN. Int J Pharm. 2024;642:123012.
44. Neugebauer P, et al. AI-enhanced process analytical technology. Biotechnol Bioeng. 2024;121:1456-1474.
45. Dabbaghian V, et al. Predictive maintenance using machine learning in pharma. AIChE J. 2023;69:e18065.
46. Srivastava K, et al. Drug repurposing in COVID-19: AI approaches. Brief Bioinform. 2021;22:2088-2103.
47. Li J, et al. Explainable AI methods for regulated drug development. Patterns. 2023;4:1007-1022.
48. McKinsey & Company. Generative AI in the pharmaceutical industry. 2024.
49. Collins FS, et al. Data sharing and AI in biomedical research. N Engl J Med. 2021;384:2271-2273.
50. Öztürk H, Özgür A, Özkirimli E. DeepDTA for binding affinity prediction. Bioinformatics. 2018;34:i821-i829.
51. Gao H, et al. Generative adversarial networks in drug discovery. Drug Discov Today. 2022;27:123-134.
52. Ragoza M, et al. Protein-ligand scoring with CNN. J Chem Inf Model. 2020;60:2014-2025.
53. Walters WP. Virtual screening in the era of deep learning. Curr Opin Chem Biol. 2020;56:1-6.