The Impact of Generative AI on the Efficiency and Accuracy of Drug Discovery
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Drug discovery traditionally is a long and expensive process. It requires huge financial investment and can possibly take more than a decade to develop a single drug. In this paper, It has been explored how generative AI is changing this process. By using advanced models, generative AI can design new drug molecules, predict their properties, improve efficiency by reducing both time and cost required for research to get precise and accurate data. These models can generate drug candidates with better binding affinity and drug like properties, making the selection process more reliable. It is also being used across different stages of drug discovery, from identifying targets to monitoring drug safety. Although generative AI has great potential to transform drug discovery by making it faster, more efficient and accurate, there are still some challenges like the need for high quality data, lack of model transparency, regulatory concerns and more real world testing and comparison. These improvements are still needed for its full adoption.
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Kadan, A., Ryczko, K., Lloyd, E., Roitberg, A., & Yamazaki, T. (2025). Guided multi-objective generative AI to enhance structure-based drug design. In Chemical Science (Vol. 16, Issue 29, pp. 13196–13210). Royal Society of Chemistry (RSC). https://doi.org/10.1039/d5sc01778e
Obi, P., Gc, J. B., Mariasoosai, C., Diyaolu, A., & Natesan, S. (2024). Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third. In Journal of Chemical Theory and Computation (Vol. 20, Issue 14, pp. 5866–5881). American Chemical Society (ACS). https://doi.org/10.1021/acs.jctc.4c00315
Zhang, O., Lin, H., Zhang, H., Zhao, H., Huang, Y., Huang, Y., Jiang, D., Hsieh, C., Pan, P., & Hou, T. (2024). Deep Lead Optimization: Leveraging Generative AI for Structural Modification. Journal of the American Chemical Society.
Lai, L., Liu, Y., Song, B., Li, K., & Zeng, X. (2025). Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review. In ACM Computing Surveys (Vol. 57, Issue 6, pp. 1–29). Association for Computing Machinery (ACM). https://doi.org/10.1145/3714455
Vora, D., Chan, A., Pangeni Pokharel, S., Liu, Q., Chen, R., Chen, B., Chen, A., Yang, L., & Chen, C.-W. (2025). CRISPR-tica.ai: A function-informed generative modeling pipeline for prioritizing drug discovery in AML. In Blood (Vol. 146, Issue Supplement 1, pp. 277–277). American Society of Hematology. https://doi.org/10.1182/blood-2025-277
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2023). Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. In Expert Opinion on Drug Discovery (Vol. 19, Issue 1, pp. 33–42). Informa UK Limited. https://doi.org/10.1080/17460441.2023.2273839
Vanier, A., Fernandez, J., Kelley, S., Alter, L., Semenzato, P., Alberti, C., Chevret, S., Costagliola, D., Cucherat, M., Falissard, B., Gueyffier, F., Lambert, J., Lengliné, E., Locher, C., Naudet, F., Porcher, R., Thiébaut, R., Vray, M., Zohar, S., … Le Guludec, D. (2023). Rapid access to innovative medicinal products while ensuring relevant health technology assessment. Position of the French National Authority for Health. In BMJ Evidence-Based Medicine (Vol. 29, Issue 1, pp. 1–5). BMJ. https://doi.org/10.1136/bmjebm-2022-112091
Mishra, H. P., & Gupta, R. (2025). Leveraging Generative AI for Drug Safety and Pharmacovigilance. In Current Reviews in Clinical and Experimental Pharmacology (Vol. 20, Issue 2, pp. 89–97). Bentham Science Publishers Ltd. https://doi.org/10.2174/0127724328311400240823062829
Abbas, M. K. G., Rassam, A., Karamshahi, F., Abunora, R., & Abouseada, M. (2024). The Role of AI in Drug Discovery. In ChemBioChem (Vol. 25, Issue 14). Wiley. https://doi.org/10.1002/cbic.202300816
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