Artificial Intelligence in Pharmaceutical R&D: Innovation in Drug Discovery and Clinical Trials
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The integration of artificial intelligence (AI) into pharmaceutical research and development (R&D) represents a paradigm shift in light of the high attrition rates, financial costs, and lengthy timelines associated with the traditional model. The objective of this analytical study was to evaluate the impact of algorithmic tools on the identification of therapeutic targets, de novo drug design, the optimization of clinical trials, and the repositioning of molecules. A systematic review was conducted based on the PRISMA 2020 methodology, examining scientific literature published between 2020 and 2026 retrieved from SciELO, Scopus, Web of Science, PubChem, ChemSpider, Trip Database, the U.S. Food and Drug Administration (FDA). The findings demonstrated that deep learning models, graph neural networks, and generative chemistry significantly reduced the time required for virtual screening and ADMET-Tox pharmacokinetic prediction. Furthermore, predictive algorithms optimized patient stratification in Phase I through III clinical trials, minimizing experimental variability and preventing failures due to lack of efficacy or cardiotoxicity. It is concluded that AI is reshaping biopharmaceutical R&D into a data-driven iterative cycle, making it essential to establish frameworks for ethical governance and prospective validation.
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Acosta Marin, V., Ramos, O., Acosta Freites, V., Aponte, O., Korsun, N., Gutierrez, F., Santiago, A. M., Ramirez, A. K., Marin, C. E., Acosta, M., & Gonzalez, C. (2024). Algoritmos de inteligencia artificial para la evaluación de mamografías: ¿Están listos? Artículo de revisión. Revista Venezolana de Cirugía, 77(2), 75–79. https://doi.org/10.48104/rvc.2024.77.2.4 DOI: https://doi.org/10.48104/RVC.2024.77.2.4
Apolo-Montero, D. A., Castillo-Romero, C. C., Samaniego-Flores, V. M., Limongi-Basantes, D. S., & Meléndez-Carrasco, G. P. (2025). Automedicación con antibióticos en Ecuador y Latinoamérica: magnitud, determinantes y propuestas de intervención. Revista Científica Ciencia y Método, 3(4), 14-23. https://doi.org/10.55813/gaea/rcym/v3/n4/94 DOI: https://doi.org/10.55813/gaea/rcym/v3/n4/94
Arroyo-Ruales, W. E., & Soria-Poma, X. (2026). Modelo de Inteligencia artificial para el diagnóstico del dengue. Revista Científica Ciencia Y Método, 4(1), 1-13. https://doi.org/10.55813/gaea/rcym/v4/n1/126 DOI: https://doi.org/10.55813/gaea/rcym/v4/n1/126
Barucci, A., Colcelli, V., De Masi, S., Falconi, M., Leo, M. C., Marzola, A., Romagnuolo, I., Sforzi, C., & Pini, R. (2026). Ethical and regulatory frameworks for artificial intelligence in clinical research: A European perspective on the Artificial Intelligence Act for ethics committees and researchers. European Cardiology Review, 21, Artículo e01. https://doi.org/10.15420/ecr.2025.59 DOI: https://doi.org/10.15420/ecr.2025.59
Borja-Aguilar, M. N., Trejo-Carvajal, L. A., & Cambizaca-Mora, G. del P. (2025). Enfermería e inteligencia artificial en la unidad de cuidados intensivos. Revisión sistemática integrativa. Revista Científica Zambos, 4(2), 54–70. https://doi.org/10.69484/rcz/v4/n2/108 DOI: https://doi.org/10.69484/rcz/v4/n2/108
Cujilan Guamán, J. A., Chele Sudiaga, N. E., Gavilanes Burnhan, V. A., Tacle Flores, J. V., & Boza Ruiz, R. A. (2025). Impacto de la inteligencia artificial en la predicción de eventos críticos en las unidades de cuidados intensivos: Implicaciones para la práctica y la toma de decisiones en enfermería. Más Vita, 7(2), 62–76. https://doi.org/10.47606/ACVEN/MV0270 DOI: https://doi.org/10.47606/ACVEN/MV0270
Ferreira, F. J., & Carneiro, A. S. (2025). AI-driven drug discovery: A comprehensive review. ACS Omega, 10(23), 23889–23903. https://doi.org/10.1021/acsomega.5c00549 DOI: https://doi.org/10.1021/acsomega.5c00549
Fontanella, F., D’Alessandro, T., Nardone, E., De Stefano, C., Vicidomini, C., & Roviello, G. N. (2026). Artificial intelligence for natural products drug discovery in neurodegenerative therapies: A review. Biomolecules, 16(1), Artículo 129. https://doi.org/10.3390/biom16010129 DOI: https://doi.org/10.3390/biom16010129
Herrera-Sánchez, P. J., & Moreira-Flores, M. M. (2023). Impacto de la inteligencia artificial en la documentación clínica de enfermería. Revista Científica Ciencia y Método, 1(1), 1–13. https://doi.org/10.55813/gaea/rcym/v1/n1/6 DOI: https://doi.org/10.55813/gaea/rcym/v1/n1/6
Mak, K. K., Wong, Y. H., & Pichika, M. R. (2024). Artificial intelligence in drug discovery and development. En F. J. Hock & M. K. Pugsley (Eds.), Drug discovery and evaluation: Safety and pharmacokinetic assays (pp. 1461–1498). Springer. https://doi.org/10.1007/978-3-031-35529-5_92 DOI: https://doi.org/10.1007/978-3-031-35529-5_92
Mehran, M. J., Mohammadzadeh, S., Bolideei, M., Barzigar, R., Haider, K. H., Jadgal, N., & Bahrami, Y. (2026). Artificial intelligence in drug discovery: Integrative advances from data to therapeutic innovation. Drug Development Research, 87(2), Artículo e70229. https://doi.org/10.1002/ddr.70229 DOI: https://doi.org/10.1002/ddr.70229
Mendoza-Falcones, L. M., Guida-Acevedo, G. N., Herrera-Esquivel, C. A., & Herrera-Sánchez, P. J. (2026). Bioética y equidad en el acceso a los servicios de salud en Ecuador y América Latina. Journal of Economic and Social Science Research, 6(1), 121–140. https://doi.org/10.55813/gaea/jessr/v6/n1/234 DOI: https://doi.org/10.55813/gaea/jessr/v6/n1/234
Montalván-Vélez, C. L., Mogrovejo-Zambrano, J. N., Romero-Vitte, I. J., & Pinargote-Carrera, M. L. D. C. (2024). Introducción a la inteligencia artificial: Conceptos básicos y aplicaciones cotidianas. Journal of Economic and Social Science Research, 4(1), 173–183. https://doi.org/10.55813/gaea/jessr/v4/n1/93 DOI: https://doi.org/10.55813/gaea/jessr/v4/n1/93
Ogungbite, A. B., & Sibiya, M. (2026). A narrative review of artificial intelligence for drug repurposing: Lessons from COVID-19 and oncology (2020–2025). CPT: Pharmacometrics & Systems Pharmacology, 15(7), Artículo e70272. https://doi.org/10.1002/psp4.70272 DOI: https://doi.org/10.1002/psp4.70272
Romero Llerena, M. A., & Pandia Yañez, E. J. (2025). La inteligencia artificial en la salud pública: Mejorando la atención médica y previniendo enfermedades. Aula Virtual, 6(13), 1686–1705. https://doi.org/10.5281/zenodo.17247148
Song, H., Liang, Z., Zhang, X., Yang, F., Zheng, M., & Xin, G. (2026). Artificial intelligence algorithms drive the deciphering of traditional Chinese medicine by analyzing the chemicalome, targetome, and bioactivome. Targetome, 2(2), Artículo e010. https://doi.org/10.48130/targetome-0026-0002 DOI: https://doi.org/10.48130/targetome-0026-0002
U.S. Food and Drug Administration. (2026a). Digital health technologies (DHTs) for drug development. https://www.fda.gov/science-research/science-and-research-special-topics/digital-health-technologies-dhts-drug-development DOI: https://doi.org/10.69645/NNLR9471
U.S. Food and Drug Administration. (2026b). Guiding principles of good AI practice in drug development. https://www.fda.gov/about-fda/artificial-intelligence-drug-development/guiding-principles-good-ai-practice-drug-development
Vaghari, D., Mohankumar, G., Tan, K., Lowe, A., Shering, C., Tino, P., & Kourtzi, Z. (2025). AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial. Nature Communications, 16, Artículo 6244. https://doi.org/10.1038/s41467-025-61355-3 DOI: https://doi.org/10.1038/s41467-025-61355-3