基于结构的深度学习模型用于蛋白质 - 连接体结合亲缘关系预测
Debby D Wang1, Wenhui Wu2,3, Ran Wang4,5,6
1School of Science and Technology, Hong Kong Metropolitan University, 81 Chung Hau Sreet, Ho Man Tin, Hong Kong, China.
Journal of cheminformatics
|January 4, 2024
概括
深度学习显示了预测蛋白质-体结合亲和力的前景,这是分子结构科学中的一个关键挑战. 本文审查了深度学习方法,为基于结构的药物发现提供了洞察力.
科学领域:
- 分子结构科学 分子结构科学
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
背景情况:
- 深度学习已经推进了分子结构科学,以AlphaFold系列为例.
- 预测蛋白质 - 连接体结合亲和力是一个关键的挑战,需要复杂的计算方法.
研究的目的:
- 审查主流基于结构的深度学习方法来预测蛋白质-配体结合亲和力.
- 评估深度学习对这个复杂问题的准备程度.
- 为比较不同深度学习模型提供统一的基础.
主要方法:
- 专注于分子表示,学习架构和深度学习模型中的模型解释性.
- 创建了现有的深度学习模型的分类.
- 在统一的基础上评估代表性模型,以便进行有效的比较.
主要成果:
- 识别和分类主流基于结构的深度学习方法.
- 提供了对模型优点和缺点的比较分析.
- 强调需要标准化的评估协议.
结论:
- 深度学习具有很大的潜力,可以推进蛋白质-连接体结合亲和力预测.
- 该综述为理解和比较该领域的深度学习模型提供了一个框架.
- 结果可以使基于结构的药物发现和相关研究领域受益.
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