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对基于机器学习的方法进行审查,以预测药物向相互作用
Wen Shi1,2, Hong Yang1, Linhai Xie3
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Health information science and systems
|April 15, 2024
概括
使用机器学习预测药物向相互作用 (DTI) 加快了药物发现. 本综述对DTI预测方法进行了分类,重点关注数据表示,传统和深度学习方法以及未来的研究方向.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 的预测对于有效的药物发现至关重要,减轻实验验证的风险和成本.
- 机器学习 (ML) 方法对于准确的DTI预测日益至关重要.
- 目前的DTI预测依赖于药物和蛋白质的各种数据表示.
研究的目的:
- 审查和分类基于机器学习的DTI预测方法.
- 强调数据表示在DTI预测中的作用.
- 为DTI预测中的深度神经网络模型提供分类学,并讨论数据集和指标.
主要方法:
- 将DTI预测方法分类为传统的ML和深度学习 (DL) 方法.
- 五种药物和四种蛋白质数据表示的划定.
- 在DTI预测中引入深度神经网络模型的新型分类学.
主要成果:
- 基于ML的DTI预测策略的全面概述.
- 对药物和蛋白质的数据表示技术的分析.
- 综合常用的数据集和评估指标,以实现实际应用.
结论:
- 机器学习,特别是深度学习,为DTI预测提供了强大的工具.
- 标准化数据表示和评估指标对于推进该领域至关重要.
- 未来的研究应该解决当前的挑战,并探索DTI预测的新方法.
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