对基于连接体的虚拟药物查深度学习方法的审查
Hongjie Wu1, Junkai Liu1, Runhua Zhang1
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Fundamental research
|August 19, 2024
概括
本综述总结了用于虚拟药物查的深度学习方法,这是一个关键的计算技术. 它分析了大型数据集上的模型性能,为加速药物发现提供了见解.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物发现是昂贵和耗时的,计算方法越来越重要.
- 随着COVID-19的爆发,人们需要更快地开发药物和疫苗.
- 深度学习 (DL) 在加速药物虚拟查方面显示出显著的希望.
研究的目的:
- 在虚拟药物查中提供深度学习方法的全面概述.
- 为了比较和分析各种DL模型的性能,用于虚拟选任务.
- 确定计算药物发现的挑战和未来方向.
主要方法:
- 介绍药物虚拟查,数据集和数据表示的基本概念.
- 对许多常见的深度学习方法的比较分析,应用于虚拟选.
- 独立评估DL模型在大规模联结体虚拟查数据集上的性能.
主要成果:
- 详细比较不同数据集大小的深度学习模型性能.
- 确定各种深度学习方法的优缺点,用于虚拟选.
- 经验数据支持用于大规模查的特定DL模型的有效性.
结论:
- 深度学习显著提高了药物虚拟查的效率和准确性.
- 需要进一步的研究来优化DL模型,以应对复杂的药物发现挑战.
- 该审查提供了利用人工智能的路线图,以加快新疗法的开发.
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