药物发现
虚拟筛选
计算机科学
计算生物学
数据科学
计算模型
机器学习
数据集成
风险分析(工程)
人工智能
生物信息学
数据挖掘
生物
医学
作者
Marina Gorostiola González,A. M. Janssen,Adriaan P. IJzerman,Laura H. Heitman,Gerard J. P. van Westen
标识
DOI:10.1016/j.drudis.2022.03.005
摘要
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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