AI integration in bioactive-oriented research: Discovery, development, and design for pharmaceutical and medical applications

Chen, H., Engkvist, O., Wang, Y., et al. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039 DOI: https://doi.org/10.1016/j.drudis.2018.01.039

Ekins, S., Puhl, A. C., Zorn, K. M., et al. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials, 18(5), 435–441. https://doi.org/10.1038/s41563-019-0338-z DOI: https://doi.org/10.1038/s41563-019-0338-z

Elton, D. C., Boukouvalas, Z., Fuge, M. D., et al. (2019). Deep learning for molecular design—a review of the state of the art. Molecular Systems Design & Engineering, 4(4), 828–849. https://doi.org/10.1039/C9ME00039A DOI: https://doi.org/10.1039/C9ME00039A

Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2 DOI: https://doi.org/10.1038/s41586-021-03819-2

Khan, M., Jiang, X., Ahmed, I., et al. (2025). Drug repurposing and AI-driven discovery of tyrosinase inhibitors, emerging strategies for skin disorders: A review. International journal of biological macromolecules, 334(Pt 1), 149073. https://doi.org/10.1016/j.ijbiomac.2025.149073 DOI: https://doi.org/10.1016/j.ijbiomac.2025.149073

Liu, X., Xu, J., Zheng, S., et al. (2025). AI-driven discovery of brain-penetrant Galectin-3 inhibitors for Alzheimer's disease therapy. Pharmacological research, 218, 107834. https://doi.org/10.1016/j.phrs.2025.107834 DOI: https://doi.org/10.1016/j.phrs.2025.107834

Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014 DOI: https://doi.org/10.1016/j.drudis.2018.11.014

Paul, D., Sanap, G., Shenoy, S., et al. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010 DOI: https://doi.org/10.1016/j.drudis.2020.10.010

Schneider, P., Walters, W. P., Plowright, A. T., et al. (2020). Rethinking drug design in the artificial intelligence era. Nature Reviews Drug Discovery, 19(5), 353–364. https://doi.org/10.1038/s41573-019-0050-3 DOI: https://doi.org/10.1038/s41573-019-0050-3

Sun, Q., Wang, H., Xie, J., et al. (2025). Computer-Aided Drug Discovery for Undruggable Targets. Chemical reviews, 125(13), 6309–6365. https://doi.org/10.1021/acs.chemrev.4c00969 DOI: https://doi.org/10.1021/acs.chemrev.4c00969

Vamathevan, J., Clark, D., Czodrowski, P., et al. (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 DOI: https://doi.org/10.1038/s41573-019-0024-5

Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z DOI: https://doi.org/10.1038/s41551-018-0305-z

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x DOI: https://doi.org/10.1038/s41587-019-0224-x