Smart Pharmaceutical Formulation and Drug Delivery Using AI

Artificial intelligence and machine learning are transforming pharmaceutical formulation and drug delivery by shifting traditional trial-and-error approaches towards data-driven, predictive methodologies. This study examines the potential of AI to enhance drug development efficiency, optimise formulation design, and address significant pharmaceutical challenges such as elevated development costs, inadequate drug solubility, and intricate formulation variables. Artificial intelligence techniques, including support vector machines, deep learning models, and artificial neural networks, facilitate the rapid analysis of large datasets to predict critical quality attributes such as stability, bioavailability, and drug release profiles. AI integration in pre-formulation studies diminishes experimental workload and accelerates decision-making by facilitating accurate predictions of physicochemical properties. AI-assisted compatibility analysis and excipient selection also make formulations work better and last longer. Self-emulsifying drug delivery systems, nanomedicine, and controlled-release formulations are advanced uses that show how AI can make treatments work better. AI also cuts down on the time and money needed for traditional methods of drug discovery, target identification, and virtual screening by a large amount. AI-driven methods are more efficient, repeatable, and scalable than human-generated formulations, but issues with data quality, regulatory acceptance, and model interpretability still exist. AI also helps with managing the lifecycle, predicting stability, and optimising drug delivery systems. This makes products better and lowers the number of failures. The combination of AI with digital prototyping and Design of Experiments helps intelligent manufacturing and formulation innovation even more. Despite these drawbacks, hybrid approaches that combine human knowledge with AI capabilities hold the key to the future of pharmaceutical sciences. All things considered, AI-driven formulation techniques have enormous potential to transform pharmaceutical research by increasing precision, shortening development times, and improving patient outcomes.

Artificial Intelligence, Drug delivery system, Designs of Experiment, Machine learning

1. Abd-algaleel, Shaymaa A., Hend M. Abdel-Bar, Abdelkader A. Metwally, and Rania M. Hathout. “Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers.” Pharmaceuticals 14, no. 7 (2021): 645. https://doi.org/10.3390/ph14070645. [Google Scholar] [Crossref]

2. Abdellatif, Ahmed A. H., Hamdoon A. Mohammed, Riaz A. Khan, et al. “Nano-Scale Delivery: A Comprehensive Review of Nano-Structured Devices, Preparative Techniques, Site-Specificity Designs, Biomedical Applications, Commercial Products, and References to Safety, Cellular Uptake, and Organ Toxicity.” Nanotechnology Reviews 10, no. 1 (2021): 1493–559. https://doi.org/10.1515/ntrev-2021-0096. [Google Scholar] [Crossref]

3. Achary, Patnala Ganga Raju. “Applications of Quantitative Structure-Activity Relationships (QSAR) Based Virtual Screening in Drug Design: A Review.” Mini-Reviews in Medicinal Chemistry 20, no. 14 (2020): 1375–88. https://doi.org/10.2174/1389557520666200429102334. [Google Scholar] [Crossref]

4. Agatonovic-Kustrin, S., and R. Beresford. “Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research.” Journal of Pharmaceutical and Biomedical Analysis 22, no. 5 (2000): 717–27. https://doi.org/10.1016/S0731-7085(99)00272-1. [Google Scholar] [Crossref]

5. Agrwal, Akansha, Rohit Kumar, Swati Maheshwari, et al. “Mathematical and Artificial Intelligence Techniques in Modern Drug Discovery: A Review.” Drug Development Research 87, no. 1 (2026): e70212. https://doi.org/10.1002/ddr.70212. [Google Scholar] [Crossref]

6. Ahirwar, Kailash, and Rahul Shukla. “Preformulation Studies: A Versatile Tool in Formulation Design.” In Drug Formulation Design, edited by Rahul Shukla, Aleksey Kuznetsov, and Akbar Ali. IntechOpen, 2023. https://doi.org/10.5772/intechopen.110346. [Google Scholar] [Crossref]

7. Ahirwar, Kailash, and Rahul Shukla. “Preformulation Studies: A Versatile Tool in Formulation Design.” In Drug Formulation Design, edited by Rahul Shukla, Aleksey Kuznetsov, and Akbar Ali. IntechOpen, 2023. https://doi.org/10.5772/intechopen.110346. [Google Scholar] [Crossref]

8. Ahirwar, Kailash, and Rahul Shukla. “Preformulation Studies: A Versatile Tool in Formulation Design.” In Drug Formulation Design, edited by Rahul Shukla, Aleksey Kuznetsov, and Akbar Ali. IntechOpen, 2023. https://doi.org/10.5772/intechopen.110346. [Google Scholar] [Crossref]

9. Alharbi, Sultan K. “Advancing Identification of Transformation Products and Predicting Their Environmental Fate: The Current State of Machine Learning and Artificial Intelligence in Antibiotic Photolysis.” Applied Sciences 16, no. 1 (2025): 267. https://doi.org/10.3390/app16010267. [Google Scholar] [Crossref]

10. Ali, Kazi Asraf, Sk Mohin, Puja Mondal, Susmita Goswami, Soumya Ghosh, and Sabyasachi Choudhuri. “Influence of Artificial Intelligence in Modern Pharmaceutical Formulation and Drug Development.” Future Journal of Pharmaceutical Sciences 10, no. 1 (2024): 53. https://doi.org/10.1186/s43094-024-00625-1. [Google Scholar] [Crossref]

11. Ali, Kazi Asraf, Sk Mohin, Puja Mondal, Susmita Goswami, Soumya Ghosh, and Sabyasachi Choudhuri. “Influence of Artificial Intelligence in Modern Pharmaceutical Formulation and Drug Development.” Future Journal of Pharmaceutical Sciences 10, no. 1 (2024): 53. https://doi.org/10.1186/s43094-024-00625-1. [Google Scholar] [Crossref]

12. Allen, Loyd V. “Dosage Form Design and Development.” Clinical Therapeutics 30, no. 11 (2008): 2102–11. https://doi.org/10.1016/j.clinthera.2008.11.015. [Google Scholar] [Crossref]

13. Álvarez-Machancoses, Óscar, and Juan Luis Fernández-Martínez. “Using Artificial Intelligence Methods to Speed up Drug Discovery.” Expert Opinion on Drug Discovery 14, no. 8 (2019): 769–77. https://doi.org/10.1080/17460441.2019.1621284. [Google Scholar] [Crossref]

14. Alves, Thais F. R., Margreet Morsink, Fernando Batain, et al. “Applications of Natural, Semi-Synthetic, and Synthetic Polymers in Cosmetic Formulations.” Cosmetics 7, no. 4 (2020): 75. https://doi.org/10.3390/cosmetics7040075. [Google Scholar] [Crossref]

15. Amer, Yousef, Chidiebere Jeffrey Ogochukwu, and Ashraf Zaghwan. “AI-Driven Circular Manufacturing Framework for Predictive Material Flow Optimisation.” Intelligent Systems with Applications 30 (May 2026): 200651. https://doi.org/10.1016/j.iswa.2026.200651. [Google Scholar] [Crossref]

16. Anklam, Elke, Martin Iain Bahl, Robert Ball, et al. “Emerging Technologies and Their Impact on Regulatory Science.” Experimental Biology and Medicine 247, no. 1 (2022): 1–75. https://doi.org/10.1177/15353702211052280. [Google Scholar] [Crossref]

17. Asif, Sohaib, Yi Wenhui, Saif- ur-Rehman, et al. “Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision.” Archives of Computational Methods in Engineering 32, no. 2 (2025): 853–83. https://doi.org/10.1007/s11831-024-10148-w. [Google Scholar] [Crossref]

18. Aumklad, Porawan, Phuvamin Suriyaamporn, Suwanee Panomsuk, Boonnada Pamornpathomkul, and Praneet Opanasopit. “Artificial Intelligence-Aided Rational Design and Prediction Model for Progesterone-Loaded Self-Microemulsifying Drug Delivery System Formulations.” Science, Engineering and Health Studies, July 18, 2024, 24050002. https://doi.org/10.69598/sehs.18.24050002. [Google Scholar] [Crossref]

19. Badini, Silvia, Stefano Regondi, and Raffaele Pugliese. “Unleashing the Power of Artificial Intelligence in Materials Design.” Materials 16, no. 17 (2023): 5927. https://doi.org/10.3390/ma16175927. [Google Scholar] [Crossref]

20. Barton, John H., and Ezekiel J. Emanuel. “The Patents-Based Pharmaceutical Development Process: Rationale, Problems, and Potential Reforms.” JAMA 294, no. 16 (2005): 2075. https://doi.org/10.1001/jama.294.16.2075. [Google Scholar] [Crossref]

21. Baveye, Philippe C., Wilfred Otten, Alexandra Kravchenko, et al. “Emergent Properties of Microbial Activity in Heterogeneous Soil Microenvironments: Different Research Approaches Are Slowly Converging, Yet Major Challenges Remain.” Frontiers in Microbiology 9 (August 2018): 1929. https://doi.org/10.3389/fmicb.2018.01929. [Google Scholar] [Crossref]

22. Benbya, Hind, Thomas H. Davenport, and Stella Pachidi. “Artificial Intelligence in Organizations: Current State and Future Opportunities.” SSRN Electronic Journal, ahead of print, 2020. https://doi.org/10.2139/ssrn.3741983. [Google Scholar] [Crossref]

23. Bhalani, Dixit V., Bhingaradiya Nutan, Avinash Kumar, and Arvind K. Singh Chandel. “Bioavailability Enhancement Techniques for Poorly Aqueous Soluble Drugs and Therapeutics.” Biomedicines 10, no. 9 (2022): 2055. https://doi.org/10.3390/biomedicines10092055. [Google Scholar] [Crossref]

24. Bhatia, Nitish, Mohd Masih Uzzaman Khan, and Saahil Arora. “The Role of Artificial Intelligence in Revolutionizing Pharmacological Research.” Current Pharmacology Reports 10, no. 6 (2024): 323–29. https://doi.org/10.1007/s40495-024-00367-x. [Google Scholar] [Crossref]

25. Bouslimani, Amina, Ricardo Da Silva, Tomasz Kosciolek, et al. “The Impact of Skin Care Products on Skin Chemistry and Microbiome Dynamics.” BMC Biology 17, no. 1 (2019): 47. https://doi.org/10.1186/s12915-019-0660-6. [Google Scholar] [Crossref]

26. Bulusu, Krishna C., Rajarshi Guha, Daniel J. Mason, et al. “Modelling of Compound Combination Effects and Applications to Efficacy and Toxicity: State-of-the-Art, Challenges and Perspectives.” Drug Discovery Today 21, no. 2 (2016): 225–38. https://doi.org/10.1016/j.drudis.2015.09.003. [Google Scholar] [Crossref]

27. Chahuan, Abhishek, Abhishek Verma, Devank Shekho, Ritika Mishra, and Ankit Awasthi. “Revolutionizing the World of Pharmaceuticals: Unleashing theGame-Changing Power of 3D Printing.” Current Drug Targets 25, no. 8 (2024): 513–16. https://doi.org/10.2174/0113894501304163240429081741. [Google Scholar] [Crossref]

28. Chaudhary, Shivang, Prakash Muthudoss, Thiagarajan Madheswaran, Amrit Paudel, and Vinod Gaikwad. “Artificial Intelligence (AI) in Drug Product Designing, Development, and Manufacturing.” In A Handbook of Artificial Intelligence in Drug Delivery. Elsevier, 2023. https://doi.org/10.1016/B978-0-323-89925-3.00015-0. [Google Scholar] [Crossref]

29. Chauhan, Shikha Baghel, Indu Singh, Ananya Dwivedi, and Asmitaa Dimri. “AI-Enhanced Personalized Skincare: Implications for Skin Microbiome Diversity and Pharmacogenomics Precision in Dermatology.” Current Pharmacogenomics and Personalized Medicine 22 (August 2025): e18756921368686. https://doi.org/10.2174/0118756921368686250621143748. [Google Scholar] [Crossref]

30. Chen, Shuangdi, Zhaofeng Li, Bimal Chitrakar, et al. “Minimum Inhibitory Concentration (MIC) of Essential Oils Against Microorganisms: Methods, Function, Accuracy Factors and Application.” Food Reviews International 40, no. 10 (2024): 3445–70. https://doi.org/10.1080/87559129.2024.2357612. [Google Scholar] [Crossref]

31. Chen, Xing, Chenggang Clarence Yan, Xiaotian Zhang, et al. “Drug–Target Interaction Prediction: Databases, Web Servers and Computational Models.” Briefings in Bioinformatics 17, no. 4 (2016): 696–712. https://doi.org/10.1093/bib/bbv066. [Google Scholar] [Crossref]

32. Chen, Yixin, Troy W. McCall, Anand R. Baichwal, and Marvin C. Meyer. “The Application of an Artificial Neural Network and Pharmacokinetic Simulations in the Design of Controlled-Release Dosage Forms.” Journal of Controlled Release 59, no. 1 (1999): 33–41. https://doi.org/10.1016/S0168-3659(98)00171-0. [Google Scholar] [Crossref]

33. Chopra, Hitesh, Annu, Dong K. Shin, et al. “Revolutionizing Clinical Trials: The Role of AI in Accelerating Medical Breakthroughs.” International Journal of Surgery 109, no. 12 (2023): 4211–20. https://doi.org/10.1097/JS9.0000000000000705. [Google Scholar] [Crossref]

34. Chou, Wei-Chun, and Zhoumeng Lin. “Machine Learning and Artificial Intelligence in Physiologically Based Pharmacokinetic Modeling.” Toxicological Sciences 191, no. 1 (2023): 1–14. https://doi.org/10.1093/toxsci/kfac101. [Google Scholar] [Crossref]

35. Cook, D. F., C. T. Ragsdale, and R. L. Major. “Combining a Neural Network with a Genetic Algorithm for Process Parameter Optimization.” Engineering Applications of Artificial Intelligence 13, no. 4 (2000): 391–96. https://doi.org/10.1016/S0952-1976(00)00021-X. [Google Scholar] [Crossref]

36. Dangeti, Appalaraju, Deekshi Gladiola Bynagari, and Krishnaveni Vydani. “Revolutionizing Drug Formulation: Harnessing Artificial Intelligence and Machine Learning for Enhanced Stability, Formulation Optimization, and Accelerated Development.” International Journal of Pharmaceutical Sciences and Medicine 8, no. 8 (2023): 18–29. https://doi.org/10.47760/ijpsm.2023.v08i08.003. [Google Scholar] [Crossref]

37. Dangeti, Appalaraju, Deekshi Gladiola Bynagari, and Krishnaveni Vydani. “Revolutionizing Drug Formulation: Harnessing Artificial Intelligence and Machine Learning for Enhanced Stability, Formulation Optimization, and Accelerated Development.” International Journal of Pharmaceutical Sciences and Medicine 8, no. 8 (2023): 18–29. https://doi.org/10.47760/ijpsm.2023.v08i08.003. [Google Scholar] [Crossref]

38. Darji, Mittal A., Rahul M. Lalge, Sushrut P. Marathe, et al. “Excipient Stability in Oral Solid Dosage Forms: A Review.” AAPS PharmSciTech 19, no. 1 (2018): 12–26. https://doi.org/10.1208/s12249-017-0864-4. [Google Scholar] [Crossref]

39. Dayan, Nava, ed. Handbook of Formulating Dermal Applications: A Definitive Practical Guide. 1st ed. Wiley, 2016. https://doi.org/10.1002/9781119364221. [Google Scholar] [Crossref]

40. Desai, Jagruti, Prince Dhameliya, and Swayamprakash Patel. “Optimizing Critical Quality Attributes of Fast Disintegrating Tablets Using Artificial Neural Networks: A Scientific Benchmark Study.” Drug Development and Industrial Pharmacy 50, no. 12 (2024): 995–1007. https://doi.org/10.1080/03639045.2024.2434640. [Google Scholar] [Crossref]

41. Desai, Jagruti, Prince Dhameliya, and Swayamprakash Patel. “Optimizing Critical Quality Attributes of Fast Disintegrating Tablets Using Artificial Neural Networks: A Scientific Benchmark Study.” Drug Development and Industrial Pharmacy 50, no. 12 (2024): 995–1007. https://doi.org/10.1080/03639045.2024.2434640. [Google Scholar] [Crossref]

42. Dey, Hrithik, Nisha Arya, Harshita Mathur, Neel Chatterjee, and Ruchi Jadon. “Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design.” International Journal of Newgen Research in Pharmacy & Healthcare, June 30, 2024, 30–41. https://doi.org/10.61554/ijnrph.v2i1.2024.67. [Google Scholar] [Crossref]

43. Dey, Hrithik, Nisha Arya, Harshita Mathur, Neel Chatterjee, and Ruchi Jadon. “Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design.” International Journal of Newgen Research in Pharmacy & Healthcare, June 30, 2024, 30–41. https://doi.org/10.61554/ijnrph.v2i1.2024.67. [Google Scholar] [Crossref]

44. Dey, Hrithik, Nisha Arya, Harshita Mathur, Neel Chatterjee, and Ruchi Jadon. “Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design.” International Journal of Newgen Research in Pharmacy & Healthcare, June 30, 2024, 30–41. https://doi.org/10.61554/ijnrph.v2i1.2024.67. [Google Scholar] [Crossref]

45. Di Guardo, Antonio, Federica Trovato, Carmen Cantisani, et al. “Artificial Intelligence in Cosmetic Formulation: Predictive Modeling for Safety, Tolerability, and Regulatory Perspectives.” Cosmetics 12, no. 4 (2025): 157. https://doi.org/10.3390/cosmetics12040157. [Google Scholar] [Crossref]

46. Di Guardo, Antonio, Federica Trovato, Carmen Cantisani, et al. “Artificial Intelligence in Cosmetic Formulation: Predictive Modeling for Safety, Tolerability, and Regulatory Perspectives.” Cosmetics 12, no. 4 (2025): 157. https://doi.org/10.3390/cosmetics12040157. [Google Scholar] [Crossref]

47. DiFeo, Thomas J. “Safety and Efficacy: The Role of Chemistry, Manufacturing, and Controls in Pharmaceutical Drug Development.” Drug Development and Industrial Pharmacy 30, no. 3 (2004): 247–57. https://doi.org/10.1081/DDC-120030428. [Google Scholar] [Crossref]

48. Duarte, Joana Galvão, Maria Galvão Duarte, Ana Paula Piedade, and Filipa Mascarenhas-Melo. “Rethinking Pharmaceutical Industry with Quality by Design: Application in Research, Development, Manufacturing, and Quality Assurance.” The AAPS Journal 27, no. 4 (2025): 96. https://doi.org/10.1208/s12248-025-01079-w. [Google Scholar] [Crossref]

49. Dumitriu Buzia, Olimpia, Ana Maria Păduraru, Claudia Simona Stefan, et al. “Strategies for Improving Transdermal Administration: New Approaches to Controlled Drug Release.” Pharmaceutics 15, no. 4 (2023): 1183. https://doi.org/10.3390/pharmaceutics15041183. [Google Scholar] [Crossref]

50. Gaikwad, Vinod L., Neela M. Bhatia, Sujit A. Desai, and Manish S. Bhatia. “Quantitative Structure Property Relationship Modeling of Excipient Properties for Prediction of Formulation Characteristics.” Carbohydrate Polymers 151 (October 2016): 593–99. https://doi.org/10.1016/j.carbpol.2016.05.114. [Google Scholar] [Crossref]

51. Gaikwad, Vinod L., Neela M. Bhatia, Sujit A. Desai, and Manish S. Bhatia. “Quantitative Structure Property Relationship Modeling of Excipient Properties for Prediction of Formulation Characteristics.” Carbohydrate Polymers 151 (October 2016): 593–99. https://doi.org/10.1016/j.carbpol.2016.05.114. [Google Scholar] [Crossref]

52. Gannot, G., Ma Cutting, Dj Fischer, and Lj Hsu. “Reproducibility and Transparency in Biomedical Sciences.” Oral Diseases 23, no. 7 (2017): 813–16. https://doi.org/10.1111/odi.12588. [Google Scholar] [Crossref]

53. Garud, Kunal Sandip, Simon Jayaraj, and Moo‐Yeon Lee. “A Review on Modeling of Solar Photovoltaic Systems Using Artificial Neural Networks, Fuzzy Logic, Genetic Algorithm and Hybrid Models.” International Journal of Energy Research 45, no. 1 (2021): 6–35. https://doi.org/10.1002/er.5608. [Google Scholar] [Crossref]

54. Ghalayini, Alaa M., and James S. Noble. “The Changing Basis of Performance Measurement.” International Journal of Operations & Production Management 16, no. 8 (1996): 63–80. https://doi.org/10.1108/01443579610125787. [Google Scholar] [Crossref]

55. Ghosh, Moumita, and A. Thirugnanam. “Introduction to Artificial Intelligence.” In Artificial Intelligence for Information Management: A Healthcare Perspective, vol. 88, edited by K. G. Srinivasa, Siddesh G. M., and S. R. Mani Sekhar. Studies in Big Data. Springer Singapore, 2021. https://doi.org/10.1007/978-981-16-0415-7_2. [Google Scholar] [Crossref]

56. Goel, Akash, Amit Kumar Goel, and Adesh Kumar. “The Role of Artificial Neural Network and Machine Learning in Utilizing Spatial Information.” Spatial Information Research 31, no. 3 (2023): 275–85. https://doi.org/10.1007/s41324-022-00494-x. [Google Scholar] [Crossref]

57. Gong, Youdi, Guangzhen Liu, Yunzhi Xue, Rui Li, and Lingzhong Meng. “A Survey on Dataset Quality in Machine Learning.” Information and Software Technology 162 (October 2023): 107268. https://doi.org/10.1016/j.infsof.2023.107268. [Google Scholar] [Crossref]

58. Grice, Elizabeth A., and Julia A. Segre. “The Skin Microbiome.” Nature Reviews Microbiology 9, no. 4 (2011): 244–53. https://doi.org/10.1038/nrmicro2537. [Google Scholar] [Crossref]

59. Grigoryan, Artur, Stefan Helfrich, Valentin Lequeux, et al. “Smart Formulation: AI-Driven Web Platform for Optimization and Stability Prediction of Compounded Pharmaceuticals Using KNIME.” Pharmaceuticals 18, no. 8 (2025): 1240. https://doi.org/10.3390/ph18081240. [Google Scholar] [Crossref]

60. Gupta, Dhruv, Anuj A. Biswas, Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, and Ashish K. Agrawal. “Advancing Pharmaceutical Intelligence via Computationally Prognosticating the In-Vitro Parameters of Fast Disintegration Tablets Using Machine Learning Models.” European Journal of Pharmaceutics and Biopharmaceutics 204 (November 2024): 114508. https://doi.org/10.1016/j.ejpb.2024.114508. [Google Scholar] [Crossref]

61. Gupta, Rohan, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K. Ambasta, and Pravir Kumar. “Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery.” Molecular Diversity 25, no. 3 (2021): 1315–60. https://doi.org/10.1007/s11030-021-10217-3. [Google Scholar] [Crossref]

62. Gupta, Rohan, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K. Ambasta, and Pravir Kumar. “Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery.” Molecular Diversity 25, no. 3 (2021): 1315–60. https://doi.org/10.1007/s11030-021-10217-3. [Google Scholar] [Crossref]

63. Gupta, Shweta, Rajesh Kesarla, and Abdelwahab Omri. “Formulation Strategies to Improve the Bioavailability of Poorly Absorbed Drugs with Special Emphasis on Self-Emulsifying Systems.” ISRN Pharmaceutics 2013 (December 2013): 1–16. https://doi.org/10.1155/2013/848043. [Google Scholar] [Crossref]

64. Gupta, Shweta, Rajesh Kesarla, and Abdelwahab Omri. “Formulation Strategies to Improve the Bioavailability of Poorly Absorbed Drugs with Special Emphasis on Self-Emulsifying Systems.” ISRN Pharmaceutics 2013 (December 2013): 1–16. https://doi.org/10.1155/2013/848043. [Google Scholar] [Crossref]

65. Hagendorff, Thilo, and Katharina Wezel. “15 Challenges for AI: Or What AI (Currently) Can’t Do.” AI & SOCIETY 35, no. 2 (2020): 355–65. https://doi.org/10.1007/s00146-019-00886-y. [Google Scholar] [Crossref]

66. Halla, Noureddine, Isabel P. Fernandes, Sandrina A. Heleno, et al. “Cosmetics Preservation: A Review on Present Strategies.” Molecules 23, no. 7 (2018): 1571. https://doi.org/10.3390/molecules23071571. [Google Scholar] [Crossref]

67. Hassan Ali. “Artificial Intelligence in Multi-Omics Data Integration: Advancing Precision Medicine, Biomarker Discovery and Genomic-Driven Disease Interventions.” International Journal of Science and Research Archive 8, no. 1 (2023): 1012–30. https://doi.org/10.30574/ijsra.2023.8.1.0189. [Google Scholar] [Crossref]

68. Hassan, Raouf, and Mohammad Reza Kazemi. “Data-Driven Frameworks to Robustly Predict Solubility Parameter of Diverse Polymers.” Scientific Reports 15, no. 1 (2025): 31157. https://doi.org/10.1038/s41598-025-12758-1. [Google Scholar] [Crossref]

69. Hassija, Vikas, Vinay Chamola, Atmesh Mahapatra, et al. “Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.” Cognitive Computation 16, no. 1 (2024): 45–74. https://doi.org/10.1007/s12559-023-10179-8. [Google Scholar] [Crossref]

70. Haykal, Diala. “Emerging and Pioneering AI Technologies in Aesthetic Dermatology: Sketching a Path Toward Personalized, Predictive, and Proactive Care.” Cosmetics 11, no. 6 (2024): 206. https://doi.org/10.3390/cosmetics11060206. [Google Scholar] [Crossref]

71. Haykal, Diala. “Emerging and Pioneering AI Technologies in Aesthetic Dermatology: Sketching a Path Toward Personalized, Predictive, and Proactive Care.” Cosmetics 11, no. 6 (2024): 206. https://doi.org/10.3390/cosmetics11060206. [Google Scholar] [Crossref]

72. Hoffmann, Rudolf, and Christoph Reich. “A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing.” Electronics 12, no. 22 (2023): 4572. https://doi.org/10.3390/electronics12224572. [Google Scholar] [Crossref]

73. Husnain, Ali, Saad Rasool, Ayesha Saeed, Ahmad Yousaf Gill, and Hafiz Khawar Hussain. “AI’S Healing Touch: Examining Machine Learning’s Transformative Effects On Healthcare.” Journal of World Science 2, no. 10 (2023): 1681–95. https://doi.org/10.58344/jws.v2i10.448. [Google Scholar] [Crossref]

74. Jamialahmadi, Hamid, Ghazaleh Khalili-Tanha, Elham Nazari, and Mostafa Rezaei-Tavirani. “Artificial Intelligence and Bioinformatics: A Journey from Traditional Techniques to Smart Approaches.” Gastroenterology and Hepatology from Bed to Bench (IR) 17, no. 3 (2024). https://doi.org/10.22037/ghfbb.v17i3.2977. [Google Scholar] [Crossref]

75. Jiang, Junhuang, Xiangyu Ma, Defang Ouyang, and Robert O. Williams. “Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms.” Pharmaceutics 14, no. 11 (2022): 2257. https://doi.org/10.3390/pharmaceutics14112257. [Google Scholar] [Crossref]

76. Johnstone, Iain M., and D. Michael Titterington. “Statistical Challenges of High-Dimensional Data.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1906 (2009): 4237–53. https://doi.org/10.1098/rsta.2009.0159. [Google Scholar] [Crossref]

77. Kakkar, Ashok, Giovanni Traverso, Omid C. Farokhzad, Ralph Weissleder, and Robert Langer. “Evolution of Macromolecular Complexity in Drug Delivery Systems.” Nature Reviews Chemistry 1, no. 8 (2017): 0063. https://doi.org/10.1038/s41570-017-0063. [Google Scholar] [Crossref]

78. Kalasz, Huba, and Istvan Antal. “Drug Excipients.” Current Medicinal Chemistry 13, no. 21 (2006): 2535–63. https://doi.org/10.2174/092986706778201648. [Google Scholar] [Crossref]

79. Khater, Tarek, Sara Awni Alkhatib, Aamna AlShehhi, et al. “Generative Artificial Intelligence Based Models Optimization towards Molecule Design Enhancement.” Journal of Cheminformatics 17, no. 1 (2025): 116. https://doi.org/10.1186/s13321-025-01059-4. [Google Scholar] [Crossref]

80. Khozeimeh, Fahime, Roohallah Alizadehsani, Mohamad Roshanzamir, Abbas Khosravi, Pouran Layegh, and Saeid Nahavandi. “An Expert System for Selecting Wart Treatment Method.” Computers in Biology and Medicine 81 (February 2017): 167–75. https://doi.org/10.1016/j.compbiomed.2017.01.001. [Google Scholar] [Crossref]

81. Kinter, Lewis B., and Joseph J. DeGeorge. “Scientific Knowledge and Technology, Animal Experimentation, and Pharmaceutical Development.” ILAR Journal 57, no. 2 (2016): 101–8. https://doi.org/10.1093/ilar/ilw027. [Google Scholar] [Crossref]

82. Kulkarni, Mangesh, Disha Joshi, and Swayamprakash Patel. “Harnessing the Power of DE-Interact (Drug-Excipient Interaction): An Artificial Intelligence (AI)-Based Predictive Tool for Drug-Excipient Interaction in Formulation Development.” Journal of Pharmaceutical Innovation 21, no. 1 (2026): 23. https://doi.org/10.1007/s12247-025-10236-9. [Google Scholar] [Crossref]

83. Kumar, Ashutosh, Ghanshyam Das Gupta, and Sarjana Raikwar. “Artificial Intelligence Technologies Used for the Assessment of PharmaceuticalExcipients.” Current Pharmaceutical Design 30, no. 6 (2024): 407–9. https://doi.org/10.2174/0113816128285827240119095013. [Google Scholar] [Crossref]

84. Kumar Dutta, Ashim. “Introductory Chapter: Surfactants in Household and Personal Care Formulations - An Overview.” In Surfactants and Detergents, edited by Ashim Kumar Dutta. IntechOpen, 2019. https://doi.org/10.5772/intechopen.89245. [Google Scholar] [Crossref]

85. Kumar Dutta, Ashim. “Introductory Chapter: Surfactants in Household and Personal Care Formulations - An Overview.” In Surfactants and Detergents, edited by Ashim Kumar Dutta. IntechOpen, 2019. https://doi.org/10.5772/intechopen.89245. [Google Scholar] [Crossref]

86. Kumar, Sethu Arun, Thirumoorthy Durai Ananda Kumar, Narasimha M. Beeraka, et al. “Machine Learning and Deep Learning in Data-Driven Decision Making of Drug Discovery and Challenges in High-Quality Data Acquisition in the Pharmaceutical Industry.” Future Medicinal Chemistry 14, no. 4 (2022): 245–70. https://doi.org/10.4155/fmc-2021-0243. [Google Scholar] [Crossref]

87. Leader, Ralph. “The Pharmaceutical Drug Development Lifecycle: A Comprehensive Examination of the Scientific, Regulatory, and Commercial Processes Governing the Discovery, Development, Approval, and Post-Marketing Surveillance of Therapeutic Agents in Modern Life Sciences.” Preprint, SSRN, 2025. https://doi.org/10.2139/ssrn.5948134. [Google Scholar] [Crossref]

88. L’Heureux, Alexandra, Katarina Grolinger, Hany F. Elyamany, and Miriam A. M. Capretz. “Machine Learning With Big Data: Challenges and Approaches.” IEEE Access 5 (2017): 7776–97. https://doi.org/10.1109/ACCESS.2017.2696365. [Google Scholar] [Crossref]

89. Li, Zhouxiao, Konstantin Christoph Koban, Thilo Ludwig Schenck, Riccardo Enzo Giunta, Qingfeng Li, and Yangbai Sun. “Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends.” Journal of Clinical Medicine 11, no. 22 (2022): 6826. https://doi.org/10.3390/jcm11226826. [Google Scholar] [Crossref]

90. Luengo, Gustavo S., Odile Aubrun, and Frederic Restagno. “Foams in Cosmetics: New Trends in Detergency, Friction, Coatings.” Current Opinion in Colloid & Interface Science 77 (June 2025): 101906. https://doi.org/10.1016/j.cocis.2025.101906. [Google Scholar] [Crossref]

91. Mandava, Sampath. “Accelerating Innovation: Artificial Intelligence for Scientific and Pharmaceutical Breakthroughs.” Preprint, SSRN, 2025. https://doi.org/10.2139/ssrn.5749982. [Google Scholar] [Crossref]

92. Mansour, Heidi M., MinJi Sohn, Abeer Al-Ghananeem, and Patrick P. DeLuca. “Materials for Pharmaceutical Dosage Forms: Molecular Pharmaceutics and Controlled Release Drug Delivery Aspects.” International Journal of Molecular Sciences 11, no. 9 (2010): 3298–322. https://doi.org/10.3390/ijms11093298. [Google Scholar] [Crossref]

93. Mansour, Heidi M., MinJi Sohn, Abeer Al-Ghananeem, and Patrick P. DeLuca. “Materials for Pharmaceutical Dosage Forms: Molecular Pharmaceutics and Controlled Release Drug Delivery Aspects.” International Journal of Molecular Sciences 11, no. 9 (2010): 3298–322. https://doi.org/10.3390/ijms11093298. [Google Scholar] [Crossref]

94. Marquez, Ronald, Maria S. Ortiz, Nelson Barrios, et al. “Surfactants Produced from Carbohydrate Derivatives: Part 2. A Review on the Value Chain, Synthesis, and the Potential Role of Artificial Intelligence within the Biorefinery Concept.” Journal of Surfactants and Detergents 28, no. 1 (2025): 25–76. https://doi.org/10.1002/jsde.12766. [Google Scholar] [Crossref]

95. Master of Science in Management Information Systems, College of Business, Lamar University, USA, Sanjai Vudugula, Sanath Kumar Chebrolu, et al. “integrating artificial intelligence in strategic business decision-making: a systematic review of predictive models.” International Journal of Scientific Interdisciplinary Research 04, no. 01 (2023): 01–26. https://doi.org/10.63125/s5skge53. [Google Scholar] [Crossref]

96. Mathew, Daniel Enemona, Deborah Uzoamaka Ebem, Anayo Chukwu Ikegwu, Pamela Eberechukwu Ukeoma, and Ngozi Fidelia Dibiaezue. “Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human.” Neural Processing Letters 57, no. 1 (2025): 16. https://doi.org/10.1007/s11063-025-11732-2. [Google Scholar] [Crossref]

97. Mazumdar, Hirak, Kamil Reza Khondakar, Suparna Das, Animesh Halder, and Ajeet Kaushik. “Artificial Intelligence for Personalized Nanomedicine; from Material Selection to Patient Outcomes.” Expert Opinion on Drug Delivery 22, no. 1 (2025): 85–108. https://doi.org/10.1080/17425247.2024.2440618. [Google Scholar] [Crossref]

98. Mirakhori, Fahimeh, and Sarfaraz K. Niazi. “Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective.” Pharmaceuticals 18, no. 1 (2025): 47. https://doi.org/10.3390/ph18010047. [Google Scholar] [Crossref]

99. Moon, M. Jae. “Searching for Inclusive Artificial Intelligence for Social Good: Participatory Governance and Policy Recommendations for Making AI More Inclusive and Benign for Society.” Public Administration Review 83, no. 6 (2023): 1496–505. https://doi.org/10.1111/puar.13648. [Google Scholar] [Crossref]

100. Nagpure, Nishita, Harshal Raut, Shubham Kamble, and Tirupati Rasala. “Redefining Preclinical Research Paradigms: AI-Driven Drug Discovery as a Transformative Approach to Accelerate Innovation, Improve Predictive Accuracy, and Reduce Reliance on Animal Testing.” Journal of Drug Delivery and Therapeutics 15, no. 10 (2025): 115–28. https://doi.org/10.22270/jddt.v15i10.7394. [Google Scholar] [Crossref]

101. Naharros‐Molinero, Almudena, María Ángela Caballo‐González, Francisco Javier De La Mata, and Sandra García‐Gallego. “Chewable Soft Gelatin Capsules: Design and Formulation Strategies for a Patient‐Preferred Dosage Alternative.” Macromolecular Materials and Engineering 311, no. 2 (2026): e00323. https://doi.org/10.1002/mame.202500323. [Google Scholar] [Crossref]

102. Obaidullah, Ahmad J., and Wael A. Mahdi. “Machine Learning-Based Analysis on Pharmaceutical Compounds Interaction with Polymer to Estimate Drug Solubility in Formulations.” Scientific Reports 15, no. 1 (2025): 23683. https://doi.org/10.1038/s41598-025-05535-7. [Google Scholar] [Crossref]

103. Obaidullah, Ahmad J., and Wael A. Mahdi. “Machine Learning-Based Analysis on Pharmaceutical Compounds Interaction with Polymer to Estimate Drug Solubility in Formulations.” Scientific Reports 15, no. 1 (2025): 23683. https://doi.org/10.1038/s41598-025-05535-7. [Google Scholar] [Crossref]

104. Ogunjobi, Taiwo Temitope, Precious Nkeiruka Ohaeri, Olamide Tayo Akintola, et al. “Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches.” Medinformatics, ahead of print, February 6, 2024. https://doi.org/10.47852/bonviewMEDIN42022335. [Google Scholar] [Crossref]

105. Panda, Pratikeswar, and Rajaram Mohapatra. “Revolutionizing Patient Safety: Machine Learning and AI for the Early Detection of Adverse Drug Reactions and Drug-Induced Toxicity.” Current Artificial Intelligence 02 (December 2024): e29503752308394. https://doi.org/10.2174/0129503752308394241013165241. [Google Scholar] [Crossref]

106. Pande, Shantanu. “Liposomes for Drug Delivery: Review of Vesicular Composition, Factors Affecting Drug Release and Drug Loading in Liposomes.” Artificial Cells, Nanomedicine, and Biotechnology 51, no. 1 (2023): 428–40. https://doi.org/10.1080/21691401.2023.2247036. [Google Scholar] [Crossref]

107. Panwar, Rohan, Anuradha Mishra, Abhisar Sahu, Syed Naved Quadri, M. Z. Abdin, and Saman Fatima. “Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.” AAPS PharmSciTech 27, no. 1 (2026): 71. https://doi.org/10.1208/s12249-025-03308-z. [Google Scholar] [Crossref]

108. Parvatikar, Prachi P., Sudha Patil, Kedar Khaparkhuntikar, et al. “Artificial Intelligence: Machine Learning Approach for Screening Large Database and Drug Discovery.” Antiviral Research 220 (December 2023): 105740. https://doi.org/10.1016/j.antiviral.2023.105740. [Google Scholar] [Crossref]

109. Pathan, Irfan, Arif Raza, Adarsh Sahu, et al. “Revolutionizing Pharmacology: AI-Powered Approaches in Molecular Modeling and ADMET Prediction.” Medicine in Drug Discovery 28 (December 2025): 100223. https://doi.org/10.1016/j.medidd.2025.100223. [Google Scholar] [Crossref]

110. Patil, Sharvari, Sandeep Kumar, Dasari Mallikarjuna Rao, and Kishore Rewatkar. “Current Regulatory Framework and Challenges for the Approval of ComplexGenerics in the US and the EU.” Current Indian Science 02 (January 2024): e2210299X269535. https://doi.org/10.2174/012210299X269535231203164108. [Google Scholar] [Crossref]

111. Qi, Chongchong, and Xiaolin Tang. “Slope Stability Prediction Using Integrated Metaheuristic and Machine Learning Approaches: A Comparative Study.” Computers & Industrial Engineering 118 (April 2018): 112–22. https://doi.org/10.1016/j.cie.2018.02.028. [Google Scholar] [Crossref]

112. Ranjbar, Shahla, Abbasali Emamjomeh, Fatemeh Sharifi, et al. “Lipid-Based Delivery Systems for Flavonoids and Flavonolignans: Liposomes, Nanoemulsions, and Solid Lipid Nanoparticles.” Pharmaceutics 15, no. 7 (2023): 1944. https://doi.org/10.3390/pharmaceutics15071944. [Google Scholar] [Crossref]

113. Rauch, Erwin, and Dominik T. Matt. “Artificial Intelligence in Design: A Look into the Future of Axiomatic Design.” In Design Engineering and Science, edited by Nam Pyo Suh, Miguel Cavique, and Joseph Timothy Foley. Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-49232-8_21. [Google Scholar] [Crossref]

114. Ritschel, W. A. “Biopharmaceutic and Pharmacokinetic Aspects in the Design of Controlled Release Peroral Drug Delivery Systems.” Drug Development and Industrial Pharmacy 15, nos. 6–7 (1989): 1073–103. https://doi.org/10.3109/03639048909043666. [Google Scholar] [Crossref]

115. Rubin, A. Erik, Srinivas Tummala, Douglas A. Both, Chenchi Wang, and Edward J. Delaney. “Emerging Technologies Supporting Chemical Process R&D and Their Increasing Impact on Productivity in the Pharmaceutical Industry.” Chemical Reviews 106, no. 7 (2006): 2794–810. https://doi.org/10.1021/cr040674i. [Google Scholar] [Crossref]

116. Sabu, Kevin Philip, and Rony Rajan Paul. “Picotechnology: Advancing Precision in Bioimaging and Disease Diagnosis.” In A Journey from Nanotechnology to Picotechnology, vol. 47, edited by Shrikaant Kulkarni. Lecture Notes in Nanoscale Science and Technology. Springer Nature Switzerland, 2026. https://doi.org/10.1007/978-3-032-07228-3_13. [Google Scholar] [Crossref]

117. Salawi, Ahmad. “Self-Emulsifying Drug Delivery Systems: A Novel Approach to Deliver Drugs.” Drug Delivery 29, no. 1 (2022): 1811–23. https://doi.org/10.1080/10717544.2022.2083724. [Google Scholar] [Crossref]

118. Sartaj, Ali, Anjali Rajora, Juveria Usmani, Sweeti Bana, Annu, and Javed Ali. “AI-Driven Drug Formulation Development: Transforming the Future of Pharmaceutical Drug Development from Discovery to Regulatory Aspects.” Journal of Pharmaceutical Innovation 21, no. 1 (2026): 54. https://doi.org/10.1007/s12247-025-10244-9. [Google Scholar] [Crossref]

119. Schierle, Felicitas, Jon Lindström Bolmgren, Mette Deleuran, Oliver Welter, and Vanessa Gebauer. “Precision Dermatology 2050: AI ‐driven Personalized Monitoring and Individualized Treatment.” Journal of the European Academy of Dermatology and Venereology 40, no. 3 (2026): 431–39. https://doi.org/10.1111/jdv.70248. [Google Scholar] [Crossref]

120. Schiff, Gordon D., David Klass, Josh Peterson, Gaurav Shah, and David W. Bates. “Linking Laboratory and Pharmacy: Opportunities for Reducing Errors and Improving Care.” Archives of Internal Medicine 163, no. 8 (2003): 893. https://doi.org/10.1001/archinte.163.8.893. [Google Scholar] [Crossref]

121. Seiler, Christian. “CHAPTER 8. Oral Controlled Release Technology and Development Strategy.” In Drug Discovery, edited by Geoffrey D. Tovey. Royal Society of Chemistry, 2018. https://doi.org/10.1039/9781782620402-00173. [Google Scholar] [Crossref]

122. Singh, Inderbir, Jaswinder Kaur, Sukhanpreet Kaur, Bibhuti Ranjan Barik, and Rakesh Pahwa. “Artificial Neural Networks and Neuro-Fuzzy Models: Applications in Pharmaceutical Product Development.” Brazilian Archives of Biology and Technology 66 (2023): e23210769. https://doi.org/10.1590/1678-4324-2023210769. [Google Scholar] [Crossref]

123. Sobolіeva, Нanna, and Anastasia Ivanova. “the personalisation revolution: how artificial intelligence is transforming the cosmetics industry, making consumers the source of innovation.” Three Seas Economic Journal 6, no. 1 (2025): 69–75. https://doi.org/10.30525/2661-5150/2025-1-11. [Google Scholar] [Crossref]

124. Spathis, Dimitris, and Panayiotis Vlamos. “Diagnosing Asthma and Chronic Obstructive Pulmonary Disease with Machine Learning.” Health Informatics Journal 25, no. 3 (2019): 811–27. https://doi.org/10.1177/1460458217723169. [Google Scholar] [Crossref]

125. Srivastava, Madhumita, Shiv Nandan, Aliza Zaidi, et al. “Artificial Intelligence Driven Applications in Analytical Chemistry, Drug Discovery, and Food Science: Advancements, Outlook, and Challenges.” ChemistrySelect 10, no. 16 (2025): e202404446. https://doi.org/10.1002/slct.202404446. [Google Scholar] [Crossref]

126. Tanoli, Ziaurrehman, Markus Vähä-Koskela, and Tero Aittokallio. “Artificial Intelligence, Machine Learning, and Drug Repurposing in Cancer.” Expert Opinion on Drug Discovery 16, no. 9 (2021): 977–89. https://doi.org/10.1080/17460441.2021.1883585. [Google Scholar] [Crossref]

127. Teshome, Ejigayehu, Sirawdink Fikreyesus Forsido, H. P. Vasantha Rupasinghe, and Ebisa Olika Keyata. “Potentials of Natural Preservatives to Enhance Food Safety and Shelf Life: A Review.” The Scientific World Journal 2022 (September 2022): 1–11. https://doi.org/10.1155/2022/9901018. [Google Scholar] [Crossref]

128. Thacharodi, Aswin, Prabhakar Singh, Ramu Meenatchi, et al. “Revolutionizing Healthcare and Medicine: The Impact of Modern Technologies for a Healthier Future—A Comprehensive Review.” Health Care Science 3, no. 5 (2024): 329–49. https://doi.org/10.1002/hcs2.115. [Google Scholar] [Crossref]

129. Tiwari, Prafulla C., Rishi Pal, Manju J. Chaudhary, and Rajendra Nath. “Artificial Intelligence Revolutionizing Drug Development: Exploring Opportunities and Challenges.” Drug Development Research 84, no. 8 (2023): 1652–63. https://doi.org/10.1002/ddr.22115. [Google Scholar] [Crossref]

130. Travitzky, Nahum, Alexander Bonet, Benjamin Dermeik, et al. “Additive Manufacturing of Ceramic‐Based Materials.” Advanced Engineering Materials 16, no. 6 (2014): 729–54. https://doi.org/10.1002/adem.201400097. [Google Scholar] [Crossref]

131. Vamathevan, Jessica, Dominic Clark, Paul Czodrowski, et al. “Applications of Machine Learning in Drug Discovery and Development.” Nature Reviews Drug Discovery 18, no. 6 (2019): 463–77. https://doi.org/10.1038/s41573-019-0024-5. [Google Scholar] [Crossref]

132. Velásco-Mejía, A., V. Vallejo-Becerra, A. U. Chávez-Ramírez, J. Torres-González, Y. Reyes-Vidal, and F. Castañeda-Zaldivar. “Modeling and Optimization of a Pharmaceutical Crystallization Process by Using Neural Networks and Genetic Algorithms.” Powder Technology 292 (May 2016): 122–28. https://doi.org/10.1016/j.powtec.2016.01.028. [Google Scholar] [Crossref]

133. Vora, Lalitkumar K., Amol D. Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K. Solanki, and Vivek P. Chavda. “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.” Pharmaceutics 15, no. 7 (2023): 1916. https://doi.org/10.3390/pharmaceutics15071916. [Google Scholar] [Crossref]

134. Vora, Lalitkumar K., Amol D. Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K. Solanki, and Vivek P. Chavda. “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.” Pharmaceutics 15, no. 7 (2023): 1916. https://doi.org/10.3390/pharmaceutics15071916. [Google Scholar] [Crossref]

135. Vora, Lalitkumar K., Amol D. Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K. Solanki, and Vivek P. Chavda. “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.” Pharmaceutics 15, no. 7 (2023): 1916. https://doi.org/10.3390/pharmaceutics15071916. [Google Scholar] [Crossref]

136. Wang, Jia-Chang, Hitesh Dommati, and Sheng-Jen Hsieh. “Review of Additive Manufacturing Methods for High-Performance Ceramic Materials.” The International Journal of Advanced Manufacturing Technology 103, nos. 5–8 (2019): 2627–47. https://doi.org/10.1007/s00170-019-03669-3. [Google Scholar] [Crossref]

137. Wang, Jun, Hota GangaRao, Ruifeng Liang, and Weiqing Liu. “Durability and Prediction Models of Fiber-Reinforced Polymer Composites under Various Environmental Conditions: A Critical Review.” Journal of Reinforced Plastics and Composites 35, no. 3 (2016): 179–211. https://doi.org/10.1177/0731684415610920. [Google Scholar] [Crossref]

138. Wang, Q., and C. R. Chatwin. “Key Issues and Developments in Modelling and Simulation-Based Methodologies for Manufacturing Systems Analysis, Design and Performance Evaluation.” The International Journal of Advanced Manufacturing Technology 25, nos. 11–12 (2005): 1254–65. https://doi.org/10.1007/s00170-003-1957-7. [Google Scholar] [Crossref]

139. Weichert, Dorina, Patrick Link, Anke Stoll, Stefan Rüping, Steffen Ihlenfeldt, and Stefan Wrobel. “A Review of Machine Learning for the Optimization of Production Processes.” The International Journal of Advanced Manufacturing Technology 104, nos. 5–8 (2019): 1889–902. https://doi.org/10.1007/s00170-019-03988-5. [Google Scholar] [Crossref]

140. Wilson, Wendy I., Yun Peng, and Larry L. Augsburger. “Generalization of a Prototype Intelligent Hybrid System for Hard Gelatin Capsule Formulation Development.” AAPS PharmSciTech 6, no. 3 (2005): E449–57. https://doi.org/10.1208/pt060356. [Google Scholar] [Crossref]

141. Wong, Carmen K., Samuel S. Ho, Bandana Saini, David E. Hibbs, and Romano A. Fois. “Standardisation of the FAERS Database: A Systematic Approach to Manually Recoding Drug Name Variants.” Pharmacoepidemiology and Drug Safety 24, no. 7 (2015): 731–37. https://doi.org/10.1002/pds.3805. [Google Scholar] [Crossref]

142. Xin, Hai, Akashdeep Singh Virk, Sabitoj Singh Virk, Foluso Akin-Ige, and Samiul Amin. “Applications of Artificial Intelligence and Machine Learning on Critical Materials Used in Cosmetics and Personal Care Formulation Design.” Current Opinion in Colloid & Interface Science 73 (October 2024): 101847. https://doi.org/10.1016/j.cocis.2024.101847. [Google Scholar] [Crossref]

143. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

144. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

145. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

146. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

147. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

148. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

149. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

150. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

151. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

152. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

153. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

154. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

155. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

156. Yadav, Amrendra Pratap, Gurdeep Singh, Mukesh Kumar Singh, and Anurag Chaudhary. “Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development.” Journal of Advanced Scientific Research 16, no. 07 (2025): 9–18. https://doi.org/10.55218/JASR.2025160702. [Google Scholar] [Crossref]

157. Yunusov, Timur. “Artificial Intelligence for Surfactant Science and Applications: A Review.” Preprint, March 16, 2026. https://doi.org/10.26434/chemrxiv.15000942/v1. [Google Scholar] [Crossref]

158. Zhang, Linda L. “Product Configuration: A Review of the State-of-the-Art and Future Research.” International Journal of Production Research 52, no. 21 (2014): 6381–98. https://doi.org/10.1080/00207543.2014.942012. [Google Scholar] [Crossref]

159. Zhang, Yu, Yongrui Xiao, Dimitrios Tsaoulidis, and Tao Chen. “An Early Decision-Making Algorithm for Accelerating Topical Drug Formulation Optimisation.” Computers & Chemical Engineering 201 (October 2025): 109224. https://doi.org/10.1016/j.compchemeng.2025.109224. [Google Scholar] [Crossref]

160. Zimmer, Markus. “Forced Degradation and Long-Term Stability Testing for Oral Drug Products: A Practical Approach.” In Methods for Stability Testing of Pharmaceuticals, edited by Sanjay Bajaj and Saranjit Singh. Methods in Pharmacology and Toxicology. Springer New York, 2018. https://doi.org/10.1007/978-1-4939-7686-7_4. [Google Scholar] [Crossref]

161. Zocca, Andrea, Paolo Colombo, Cynthia M. Gomes, and Jens Günster. “Additive Manufacturing of Ceramics: Issues, Potentialities, and Opportunities.” Journal of the American Ceramic Society 98, no. 7 (2015): 1983–2001. https://doi.org/10.1111/jace.13700. [Google Scholar] [Crossref]

162. Zong, Zhijuan, and Yu Guan. “AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency.” Journal of the Knowledge Economy 16, no. 1 (2024): 864–903. https://doi.org/10.1007/s13132-024-02001-z. [Google Scholar] [Crossref]

163. Bourquin, Jacques, Heinz Schmidli, Peter Van Hoogevest, and Hans Leuenberger. “Comparison of Artificial Neural Networks (ANN) with Classical Modelling Techniques Using Different Experimental Designs and Data from a Galenical Study on a Solid Dosage Form.” European Journal of Pharmaceutical Sciences 6, no. 4 (1998): 287–300. https://doi.org/10.1016/S0928-0987(97)10025-2. [Google Scholar] [Crossref]

164. Chauhan, Shikha Baghel, Indu Singh, Manya Singh, and Chirag Jain. “AI-Powered Excipient Innovation: Transforming Drug Design, ADMETProfiling, and Formulation Developmen.” Current Topics in Medicinal Chemistry 26 (March 2026). https://doi.org/10.2174/0115680266431788260223072453. [Google Scholar] [Crossref]

165. Damian, Festo, Mohammad Harati, Jeff Schwartzenhauer, Owen Van Cauwenberghe, and Shawn D. Wettig. “Challenges of Dissolution Methods Development for Soft Gelatin Capsules.” Pharmaceutics 13, no. 2 (2021): 214. https://doi.org/10.3390/pharmaceutics13020214. [Google Scholar] [Crossref]

166. Desai, Jagruti, Prince Dhameliya, and Swayamprakash Patel. “Optimizing Critical Quality Attributes of Fast Disintegrating Tablets Using Artificial Neural Networks: A Scientific Benchmark Study.” Drug Development and Industrial Pharmacy 50, no. 12 (2024): 995–1007. https://doi.org/10.1080/03639045.2024.2434640. [Google Scholar] [Crossref]

167. Ferreira, Tiago, Artur Cavaco‐Paulo, and Tarsila G. Castro. “Artificial Intelligence, Molecular Dynamics, and Beyond: Computational Insights In Cosmetics Research and Formulation Design.” ChemPlusChem 90, no. 10 (2025): e202500340. https://doi.org/10.1002/cplu.202500340. [Google Scholar] [Crossref]

168. Manyikana, Martina, Yahya E. Choonara, Lomas K. Tomar, et al. “A Review of Formulation Techniques That Impact the Disintegration and Mechanical Properties of Oradispersible Drug Delivery Technologies.” Pharmaceutical Development and Technology 21, no. 3 (2016): 354–66. https://doi.org/10.3109/10837450.2014.996897. [Google Scholar] [Crossref]

169. Panakanti, Ravikiran, and Ajit S. Narang. “Impact of Excipient Interactions on Drug Bioavailability from Solid Dosage Forms.” Pharmaceutical Research 29, no. 10 (2012): 2639–59. https://doi.org/10.1007/s11095-012-0767-8. [Google Scholar] [Crossref]

170. Rakotonirina, Adèle, Tatiana Galperine, and Eric Allémann. “Fecal Microbiota Transplantation: A Review on Current Formulations in Clostridioides Difficile Infection and Future Outlooks.” Expert Opinion on Biological Therapy 22, no. 7 (2022): 929–44. https://doi.org/10.1080/14712598.2022.2095901. [Google Scholar] [Crossref]

171. Singh, Inderbir, Jaswinder Kaur, Sukhanpreet Kaur, Bibhuti Ranjan Barik, and Rakesh Pahwa. “Artificial Neural Networks and Neuro-Fuzzy Models: Applications in Pharmaceutical Product Development.” Brazilian Archives of Biology and Technology 66 (2023): e23210769. https://doi.org/10.1590/1678-4324-2023210769. [Google Scholar] [Crossref]

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