Abstract
References
- Zhu, W., Liu, X., Li, Q., Gao, F., Liu, T., Chen, X., ... & Zhavoronkov, A. (2023). Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models. Bioorganic & Medicinal Chemistry, 91, 117414. doi: 10.1016/j.bmc.2023.117414
Abstract
Drug discovery as an important scientific area that serves human health, requires continuous advancement for improved quality of life and survival rates. However, drug discovery is a long and expensive process. The studies aimed at dealing with these problems have enabled to combination of AI with drug development stages. For every step of the R&D process, AI plays a vital role in facilitating and accelerating the work. Firstly, AI methods (deep learning and convolutional neural networks) help predict the 3D structure of an unresolved protein making it easier for the rational design of compounds to target a specific protein among other potential outcomes. After estimation of the protein structure of interest, it is also possible to determine the protein-ligand interactions by utilizing AI technologies like random forest. The other stage, namely finding the hit compounds is also possible through AI-assisted QSAR models such as deep neural networks. Besides, there are many AI methods (k-nearest neighbor and support vector machines) for ADMET prediction to optimize lead compounds. Finally, AI techniques also aid in choosing the most suitable synthesis plan. In the light of the latest advances, AI has become the focus of the pharmaceutical industry. However, despite the potential benefits of AI in drug discovery, several challenges must be considered including the availability of suitable data and bioethical issues. This article provides a comprehensive review of the benefits and applications of AI in various stages of drug discovery. In addition, the open-source act model and bioethical issues are also discussed.
Ethical Statement
The authors declare that there are no conflict of interest.
References
- Zhu, W., Liu, X., Li, Q., Gao, F., Liu, T., Chen, X., ... & Zhavoronkov, A. (2023). Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models. Bioorganic & Medicinal Chemistry, 91, 117414. doi: 10.1016/j.bmc.2023.117414
Details
| Primary Language | English |
|---|---|
| Subjects | Pharmaceutical Chemistry |
| Journal Section | Reviews |
| Authors | |
| Publication Date | October 23, 2024 |
| Submission Date | December 29, 2023 |
| Acceptance Date | September 24, 2024 |
| Published in Issue | Year 2024 |