Artificial Intelligence in ADMET Prediction: Transforming Drug Safety and Pharmacokinetic Assessment

Authors

  • Md Shimul Bhuia Associate editor, Journal of Medicinal Chemistry and Therapeutics Author

DOI:

https://doi.org/10.71193/jmct.20260013

Downloads

Download data is not yet available.

References

Bender, A., & Cortés-Ciriano, I. (2021). Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug discovery today, 26(2), 511-524. https://doi.org/10.1016/j.drudis.2020.12.009

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (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

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

Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D productivity: The pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery, 9(3), 203–214. https://doi.org/10.1038/nrd3078

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., 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

Downloads

Published

2026-07-25

How to Cite

Bhuia, M. S. . (2026). Artificial Intelligence in ADMET Prediction: Transforming Drug Safety and Pharmacokinetic Assessment . Journal of Medicinal Chemistry and Therapeutics, 2(01), 1-2. https://doi.org/10.71193/jmct.20260013