Open Access
| Issue |
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence & Machine Learning | |
| DOI | https://doi.org/10.1051/itmconf/20257601010 | |
| Published online | 25 March 2025 | |
- Boran, A. M., & Gupta, S. K. (2022). Machine learning for drug discovery: A survey of algorithms, applications, and future directions. Journal of Pharmaceutical Sciences, 111(1), 118–129. https://doi.org/10.1016/j.xphs.2021.11.021 [Google Scholar]
- Vamathevan, J., Clark, J., Czodrowski, P., et al. (2021). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 20(3), 154–168. https://doi.org/10.1038/s41573-020-00099-9 [Google Scholar]
- Liu, X., Wang, Z., & Zhang, Z. (2023). Artificial intelligence in drug design: From concept to application. Drug Discovery Today, 28(6), 1045–1053. https://doi.org/10.1016/j.drudis.2023.02.001 [Google Scholar]
- Zhavoronkov, A., & Aliper, A. (2022). Deep learning for drug discovery and biomarker development: Opportunities and challenges. Frontiers in Pharmacology, 13, 778–792. https://doi.org/10.3389/fphar.2022.1084157 [Google Scholar]
- Hussain, A., Khan, A. R., & Khalid, A. (2021). AI-based algorithms for drug screening and virtual screening: Innovations and future directions. Computational Biology and Chemistry, 90, 107380. https://doi.org/10.1016/j.compbiolchem.2021.107380 [Google Scholar]
- Yu, H., Chen, L., & Zheng, X. (2024). Deep learning in drug discovery and design: A survey and perspectives. Journal of Chemical Information and Modeling, 64(1), 8–22. https://doi.org/10.1021/acs.jcim.3c01125 [Google Scholar]
- Bawa, H. S., Padhy, M., & Sahu, S. K. (2023). Machine learning algorithms for molecular property prediction and drug design. Computational and Structural Biotechnology Journal, 21, 1485–1501. https://doi.org/10.1016/j.csbj.2023.05.015 [MathSciNet] [Google Scholar]
- Gao, M., & Yang, J. (2021). Advances in artificial intelligence for drug discovery: Machine learning methods and applications. Medicinal Research Reviews, 41(6), 2477–2500. https://doi.org/10.1002/med.21798 [Google Scholar]
- Wang, L., Zhang, X., & Liu, M. (2022). Predicting drug interactions with deep learning models. Journal of Cheminformatics, 14(1), 55. https://doi.org/10.1186/s13321-022-00595-w [Google Scholar]
- Kim, Y., Lee, D., & Ryu, H. (2023). Recent advances in artificial intelligence for drug discovery: From drug-target interactions to clinical trials. Pharmacology & Therapeutics, 239, 108276. https://doi.org/10.1016/j.pharmthera.2022.108276 [Google Scholar]
- Tan, Y., Liu, J., & Zhang, W. (2024). AI in drug repurposing: A systematic review. Artificial Intelligence in Medicine, 118, 102062. https://doi.org/10.1016/j.artmed.2022.102062 [Google Scholar]
- Xue, X., & Zhang, X. (2022). Artificial intelligence in drug discovery: Current challenges and future perspectives. Nature Reviews Drug Discovery, 21(6), 373–388. https://doi.org/10.1038/s41573-022-00414-0 [Google Scholar]
- Cao, Y., & Zhang, L. (2024). Drug discovery using machine learning and data science: A survey. International Journal of Molecular Sciences, 25(2), 274. https://doi.org/10.3390/ijms25020274 [Google Scholar]
- Zhou, Q., Chen, L., & Du, Y. (2023). A deep learning approach for novel drug discovery. Bioinformatics, 39(6), 888–895. https://doi.org/10.1093/bioinformatics/btz822 [Google Scholar]
- Aliper, A., & Zhavoronkov, A. (2022). Deep learning applications in drug discovery: From computational models to clinical trials. Bioinformatics Advances, 2(1), 1–11. https://doi.org/10.1093/bioadv/vbac024 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.