3066-280XISSN (Online)
Volume 3 , Issue 1 , PP: 12-22, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Faustino D. Reyes 1 *
Doi: https://doi.org/10.54216/MOR.030102
Artificial Intelligence (AI) has become a revolutionary solution in drug discovery and development in aspects including high costs, long times, and high failure rates. This review describes the development and focuses on areas where AI has been used for target identification, lead optimization, design of new drugs from scratch and drug repurposing. Deep learning frameworks such as generative adversarial networks (GANs), variational autoencoders (VAEs), and explainable AI (XAI) approaches have been instrumental and comparative progress in enhancing the efficacy and specificity of drug discovery processes. AI has made advances in clinical trials, trial conduct, and participant selection, as well as enhanced patient-tailored therapies for personalized medicine. Issues such as data credibility, model explainability, and algorithmic biases are still present, and logical and social sciences' cooperation and code of conduct are needed. As such, this review aligns current developments with these challenges to demonstrate the possibilities of AI in revolutionizing pharma research and enhancing health solutions worldwide.
Artificial Intelligence , Machine Learning , Drug Discovery , Generative Models , Clinical Trials , Personalized Medicine.
[1] D. Crucitti et al., “De novo drug design through artificial intelligence: an introduction,” Frontiers in Hematology, vol. 3, p. 1305741, Jan. 2024, doi: 10.3389/FRHEM.2024.1305741.
[2] M. Abbasi et al., “Designing optimized drug candidates with Generative Adversarial Network,” J Cheminform, vol. 14, no. 1, pp. 1–16, Dec. 2022, doi: 10.1186/S13321-022-00623-6/FIGURES/12.
[3] D. R. Serrano et al., “Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine,” Pharmaceutics 2024, Vol. 16, Page 1328, vol. 16, no. 10, p. 1328, Oct. 2024, doi: 10.3390/PHARMACEUTICS16101328.
[4] D. Crucitti et al., “De novo drug design through artificial intelligence: an introduction,” Frontiers in Hematology, vol. 3, p. 1305741, Jan. 2024, doi: 10.3389/FRHEM.2024.1305741.
[5] A. Blasiak, J. Khong, and T. Kee, “CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence,” SLAS Technol, vol. 25, no. 2, pp. 95–105, Apr. 2020, doi: 10.1177/2472630319890316/ASSET/6D78EAD6-3F1A-47B9-B21F-279134B39D68/MAIN.ASSETS/FX1.JPG.
[6] C. Carini, A. A. Seyhan, C. Carini claudiocarini, and click A. Attila Seyhan, “Tribulations and future opportunities for artificial intelligence in precision medicine,” Journal of Translational Medicine 2024 22:1, vol. 22, no. 1, pp. 1–31, Apr. 2024, doi: 10.1186/S12967-024-05067-0.
[7] C. Carini, A. A. Seyhan, C. Carini claudiocarini, and click A. Attila Seyhan, “Tribulations and future opportunities for artificial intelligence in precision medicine,” Journal of Translational Medicine 2024 22:1, vol. 22, no. 1, pp. 1–31, Apr. 2024, doi: 10.1186/S12967-024-05067-0.
[8] M. Proietti, A. Ragno, B. La Rosa, R. Ragno, and R. Capobianco, “Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening,” Mach Learn, vol. 113, no. 4, pp. 2013–2044, Apr. 2024, doi: 10.1007/S10994-023-06369-Y/TABLES/13.
[9] F. Ren et al., “AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor,” Jan. 2022, Accessed: Dec. 18, 2024. [Online]. Available: https://arxiv.org/abs/2201.09647v2
[10] R. Alizadehsani et al., “Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey,” IEEE Access, vol. 12, pp. 35796–35812, Sep. 2023, doi: 10.1109/ACCESS.2024.3373195.
[11] R. Özçelik, D. van Tilborg, J. Jiménez-Luna, and F. Grisoni, “Structure-based drug discovery with deep learning,” ChemBioChem, vol. 24, no. 13, Dec. 2022, doi: 10.1002/cbic.202200776.
[12] M. T. Nguyen, T. Nguyen, and T. Tran, “Learning to Discover Medicines,” Int J Data Sci Anal, vol. 16, no. 3, pp. 301–316, Feb. 2022, doi: 10.1007/s41060-022-00371-8.
[13] M. W. Mullowney et al., “Artificial intelligence for natural product drug discovery,” Nat Rev Drug Discov, vol. 22, no. 11, pp. 895–916, Sep. 2023, doi: 10.1038/S41573-023-00774-7.
[14] J. Cheng et al., “Accurate proteome-wide missense variant effect prediction with AlphaMissense,” Science (1979), vol. 381, no. 6664, Sep. 2023, doi: 10.1126/SCIENCE.ADG7492.
[15] A. Mullard, “When can AI deliver the drug discovery hits?,” Nat Rev Drug Discov, vol. 23, no. 3, pp. 159–161, Mar. 2024, doi: 10.1038/D41573-024-00036-0.
[16] “(PDF) Innovative Therapeutic Pathways in Colorectal Cancer: An AI- Assisted Personalized Medicine Approach.” Accessed: Dec. 19, 2024. [Online]. Available: https://www.researchgate.net/publication/387089287_Innovative_Therapeutic_Pathways_in_Colorectal_Cancer_An_AI-_Assisted_Personalized_Medicine_Approach
[17] S. Gandhi, V. Avhad, K. Raut, N. Javane, and S. Katkhade, “INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH AND TECHNOLOGY 12 | P a g e Artificial Intelligence in Pharmacy: A Boon for Drug Delivery & Drug Discovery,” Int. J. Sci. R. Tech, vol. 2024, no. 12, p. 1, Accessed: Dec. 19, 2024. [Online]. Available: www.ijsrtjournal.com
[18] Q. Jiang, S. Yang, S. He, and F. Li, “AI drug discovery tools and analysis technology: New Methods Aids in Studying the Compatibility of Traditional Chinese Medicine,” Pharmacological Research - Modern Chinese Medicine, p. 100566, Dec. 2024, doi: 10.1016/J.PRMCM.2024.100566.
[19] Y. Zhang et al., “Artificial Intelligence in Clinical Trials of Lung Cancer: Current and Future Prospects,” Intelligent Oncology, Dec. 2024, doi: 10.1016/J.INTONC.2024.11.003.
[20] H. Iwata, “Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency,” Bioanalysis, Dec. 2024, doi: 10.1080/17576180.2024.2437283.
[21] S. Ahmad et al., “Generative AI in Drug Designing: Current State-of-the-Art and Perspectives,” pp. 427–463, 2024, doi: 10.1007/978-981-97-8460-8_20.
[22] S. Ambreen, M. Umar, A. Noor, H. Jain, and R. Ali, “Advanced AI and ML frameworks for Transforming Drug Discovery and Optimization: With Innovative insights in Polypharmacology, Drug Repurposing, Combination Therapy and Nanomedicine.,” Eur J Med Chem, p. 117164, Dec. 2024, doi: 10.1016/J.EJMECH.2024.117164.
[23] D. R. Serrano et al., “Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine,” Pharmaceutics 2024, Vol. 16, Page 1328, vol. 16, no. 10, p. 1328, Oct. 2024, doi: 10.3390/PHARMACEUTICS16101328.
[24] I. Ghebrehiwet, N. Zaki, R. Damseh, and M. S. Mohamad, “Revolutionizing personalized medicine with generative AI: a systematic review,” Artif Intell Rev, vol. 57, no. 5, pp. 1–41, May 2024, doi: 10.1007/S10462-024-10768-5/TABLES/6.
[25] R. Alizadehsani et al., “Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey,” IEEE Access, vol. 12, pp. 35796–35812, Sep. 2023, doi: 10.1109/ACCESS.2024.3373195.
[26] R. Alizadehsani et al., “Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey,” IEEE Access, vol. 12, pp. 35796–35812, Sep. 2023, doi: 10.1109/ACCESS.2024.3373195.
