Journal
DRUG DISCOVERY TODAY
Volume 27, Issue 6, Pages 1661-1670Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.drudis.2022.03.005
Keywords
Cancer; Arti ficial intelligence; Machine learning; Structure-based drug design; Hallmarks of cancer
Categories
Funding
- Oncode Institute
Ask authors/readers for more resources
The integration of machine learning and structure-based methods has proven valuable in early drug discovery, particularly in addressing the diversity of cancer types. This article reviews six use cases of integrated computational methods and discusses their limitations and potential.
The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available