期刊
DRUG DISCOVERY TODAY
卷 27, 期 6, 页码 1661-1670出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.drudis.2022.03.005
关键词
Cancer; Arti ficial intelligence; Machine learning; Structure-based drug design; Hallmarks of cancer
资金
- Oncode Institute
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.
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