Inteligencia artificial generativa y descubrimiento de fármacos: reconfiguración del estándar de la persona experta en la materia
DOI:
https://doi.org/10.26422/RIPI.2026.2400.ramPalabras clave:
inteligencia artificial, actividad inventiva, no obviedad, descubrimiento farmacéutico, derecho de patentesResumen
La arquitectura contemporánea del derecho de patentes descansa sobre un estándar antropocéntrico de actividad inventiva que presupone límites cognitivos propios de la investigación humana, presupuesto que la integración sistemática de inteligencia artificial generativa en el descubrimiento farmacéutico ha comenzado a desestabilizar al convertir la exploración masiva del espacio químico en una práctica ordinaria. El objetivo del artículo es analizar la reconfiguración del estándar PSA/PHOSITA ante la incorporación estructural de herramientas de inteligencia artificial en el descubrimiento de fármacos, evaluando el desplazamiento de la frontera entre optimización técnica rutinaria y actividad inventiva. Los resultados identifican una tensión sistémica: la expansión de la capacidad exploratoria atribuible al experto medio genera confusión entre accesibilidad computacional y previsibilidad técnica, erosiona la doctrina del obvious to try y produce fragmentación regulatoria, lo que exige criterios que distingan generación algorítmica de selección inventiva y revaloricen las consideraciones secundarias. Se concluye que preservar la función incentivadora y divulgativa del sistema requiere mantener un estándar fundamentalmente humano que incorpore competencia algorítmica sin reducir la inventiva a mera potencia de cálculo.
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Referencias
Abbott, R. (2016). I think, therefore I invent: Creative computers and the future of patent law. Boston College Law Review, 57(4), 1079-1126.
Abbott, R. (2019). Everything is obvious. UCLA Law Review, 66(1), 2-52.
Almarzoqi, R. y Albakjaji, M. (2022). The patentability of AI invention: The case of the Kingdom of Saudi Arabia law. International Journal of Service Science, Management, Engineering, and Technology, 13(1), 1-21. https://doi.org/10.4018/IJSSMET.307111
Anfinsen, C. B. (1973). Principles that govern the folding of protein chains. Science, 181(4096), 223-230. https://doi.org/10.1126/science.181.4096.223
Aplin, T. y Liddicoat, J. (2023). Discussion paper on the interplay between patents and trade secrets in medical technologies. World Intellectual Property Organization. https://www.wipo.int/meetings/en/doc_details.jsp?doc_id=621851
Bhat, A. R. y Ahmed, S. (2025). Artificial intelligence (AI) in drug design and discovery: A comprehensive review. In Silico Research in Biomedicine, 1, 100049. https://doi.org/10.1016/j.insi.2025.100049
Bohacek, R. S., McMartin, C. y Guida, W. C. (1996). The art and practice of structure-based drug design: A molecular modeling perspective. Medicinal Research Reviews, 16(1), 3-50.
De Rassenfosse, G., Jaffe, A. B. y Wasserman, M. (2024). AI-generated inventions: Implications for the patent system. Southern California Law Review, 96, 1453-1478. https://dx.doi.org/10.2139/ssrn.4434054
Dharmasivam, M., Kaya, B., Akinware, A., Gholam Azad, M. y Richardson, D. R. (2025). Leading artificial intelligence-driven drug discovery platforms: 2025 landscape and global outlook. Pharmacological Reviews, 78, 100102. https://doi.org/10.1016/j.pharmr.2025.100102
Ding, W. y Deng, S. (2025). The patentability of AI-generated technical solutions and institutional responses: Chinese perspective vs. other countries. Information, 16(8), 629. https://doi.org/10.3390/info16080629
Dhulap, S. y Kulkarni, M. G. (2018). Prima facie obviousness of pharmaceutical patents: Implications for enantiomers. World Patent Information, 54, 39-45. https://doi.org/10.1016/j.wpi.2018.07.008
European Patent Office. (2026). Guidelines for Examination in the European Patent Office: G-VII, 3. Person skilled in the art. https://www.epo.org/en/legal/guidelines-epc/2026/g_vii_3.html
Fabris, D. (2020). From the PHOSITA to the MOSITA: Will “secondary considerations” save pharmaceutical patents from artificial intelligence? IIC - International Review of Intellectual Property and Competition Law, 51, 685-708. https://doi.org/10.1007/s40319-020-00953-8
Gangwal, A. y Lavecchia, A. (2026). Explainable AI methods for drug discovery: A survey of interpretability, metrics and mechanistic insight. Computer Science Review, 61, 100943. https://doi.org/10.1016/j.cosrev.2026.100943
Huang, P.-S., Boyken, S. E. y Baker, D. (2016). The coming of age of de novo protein design. Nature, 537(7620), 320-327. https://doi.org/10.1038/nature19946
Igbokwe, E. M. (2024). Human to machine innovation: Does legal personhood and inventorship threshold offer any leeway? The Journal of World Intellectual Property, 27(2), 1-17. https://doi.org/10.1111/jwip.12294
Jiang, L., Althoff, E. A., Clemente, F. R., Doyle, L., Röthlisberger, D., Zanghellini, A., Gallaher, J. L., Betker, J. L., Tanaka, F., Barbas, III, C. F., Hilvert, D., Houk, K. N., Stoddard, B. L. y Baker, D. (2008). De novo computational design of retro-aldol enzymes. Science, 319(5868), 1387-1391. https://doi.org/10.1126/science.1152692
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J. … y Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583-589. https://doi.org/10.1038/s41586-021-03819-2
Kant, S. y Roy, D. S. (2025). Artificial intelligence in drug discovery and development: Transforming challenges into opportunities. Discover Pharmaceutical Sciences, 1(7). https://doi.org/10.1007/s44395-025-00007-3
Kuhlman, B., Dantas, G., Ireton, G. C., Varani, G., Stoddard, B. L. y Baker, D. (2003). Design of a novel globular protein fold with atomic-level accuracy. Science, 302(5649), 1364-1368. https://doi.org/10.1126/science.1089427
Kunda, I. (2024). Artificial intelligence as a challenge for European patent law. En Proceedings of the 2024 47th MIPRO ICT and Electronics Convention (MIPRO) (pp. 1476-1482). IEEE. https://doi.org/10.1109/MIPRO60963.2024.10569722
Lee, H., Kim, J., Kim, J. W. y Lee, Y. (2025). Recent advances in AI-based toxicity prediction for drug discovery. Frontiers in Chemistry, 13, 1632046. https://doi.org/10.3389/fchem.2025.1632046
Levinthal, C. (1969). How to fold graciously. En Mössbauer Spectroscopy in Biological Systems Proceedings (pp. 22-24). University of Illinois Bulletin.
Lopes, A. B., Rodrigues, C. F. y Silva, F. A. M. (2026). From algorithm to medicine: AI in the discovery and development of new drugs. AI, 7(26), 1-28. https://doi.org/10.3390/ai7010026
Patel, V. y Shah, M. (2022). Artificial intelligence and machine learning in drug discovery and development. Intelligent Medicine, 2(3), 134-140. https://doi.org/10.1016/j.imed.2021.10.001
Pereira, D. J. y Kunin, S. G. (2015). What is your reasonable expectation of success in obtaining pharmaceutical or biotechnology patents having nonobvious claimed inventions that the courts will uphold? An overview of obviousness court decisions. Cold Spring Harbor Perspectives in Medicine, 5(4), a020875. https://doi.org/10.1101/cshperspect.a020875
Ramalho, A. (2018). Patentability of AI-generated inventions: Is a reform of the patent system needed? SSRN Working Paper. https://doi.org/10.2139/ssrn.3168703
Roşca, C. y Repanovici, A. (2025). How to consider industrial property and patents when using artificial intelligence for creation. The International Maritime Transport and Logistics (MARLOG 14), 114-121. https://dx.doi.org/10.21622/MARLOG.2025.14.1.59
Schellekens, M. (2022). Artificial intelligence and the re-imagination of inventive step. JIPITEC - Journal of Intellectual Property, Information Technology and E-Commerce Law, 13(2), 89-98.
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K. y Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706-710. https://doi.org/10.1038/s41586-019-1923-7
Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, G., Ruiz, H. K., Rawat, S., Kumar, D. y Lalatsa, A. (2024). Artificial intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine. Pharmaceutics, 16(10), 1328. https://doi.org/10.3390/pharmaceutics16101328
Simon, B. M. (2013). The implications of technological advancement for obviousness. Michigan Telecommunications and Technology Law Review, 19(2), 331-376.
Soans, C. A. (1966). Some absurd presumptions in patent cases. Patent, Trademark and Copyright Journal of Research and Education, 10, 433-449.
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R. y Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702. https://doi.org/10.1016/j.cell.2020.01.021
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A. … y Hassabis, D. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596, 590-596. https://doi.org/10.1038/s41586-021-03828-1
United States Patent and Trademark Office. (2024a). Request for comments on the impact of the proliferation of artificial intelligence on prior art, the knowledge of a PHOSITA, and determinations of patentability. 89 Fed. Reg. 34217.
United States Patent and Trademark Office. (2024b). Inventorship guidance for AI-assisted inventions. 89 Fed. Reg. 10043.
United States Patent and Trademark Office. (2025). Revised inventorship guidance for AI-assisted inventions. 90 Fed. Reg. 227.
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitze, M. y Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463-477. https://doi.org/10.1038/s41573-019-0024-5
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. y Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 5998-6008). https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Verma, V. y Kumar, D. (2026). Artificial intelligence and machine learning in drug discovery: From lead discovery to clinical validation (2020-2025). Letters in Drug Design & Discovery. https://doi.org/10.1016/j.lddd.2026.100341
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