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迈耶尔-同类学学习预测蛋白质-质结合的亲和关系

Hongsong Feng¹, Li Shen¹, Jian Liu^2,1

¹Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.

Journal of computational biophysics and chemistry

|September 29, 2025

概括

持久的梅耶尔同质 (PMH) 理论增强了人工智能药物设计的分子表征. 这种新的方法改善了对蛋白质 - 配体结合亲缘关系的预测,推进了制药领域的机器学习.

科学领域:

计算化学是一种计算化学.
拓学数据分析的分析.
机器学习机器学习

背景情况:

人工智能 (AI) 正在改变药物设计,需要有效的分子特征用于机器学习 (ML) 模型.
先进的数学技术,特别是拓学,对于提取有意义的分子描述符至关重要.
持久同质理论通过分析拓不变量,提供了对分子结构的洞察.

研究的目的:

引入持久的梅耶尔同质 (PMH) 理论,以在多个尺度上获得更丰富的拓信息.
开发一种新的多尺度拓向量化方法,用于使用PMH进行分子表示.
增强分子数据的描述性和预测性分析,用于人工智能驱动的药物发现.

主要方法:

扩展标准同理学理论使用迈尔同理学与满足d^N = 0 (N >= 2) 的概括差异.
开发和应用持久梅耶尔同质 (PMH) 理论.
为分子表示创建一个多尺度拓向量化.
在蛋白质-联结体数据集上的基准测试 (PDBbind-v2007,PDBbind-v2013,PDBbind-v2016).

主要成果:

与标准方法相比,PMH理论提供了更丰富的拓信息.
新的多尺度拓向量化有效地表示分子数据.
迈耶尔同学模型在基准数据集上预测蛋白质-联体结合亲缘关系方面表现出卓越的性能.

结论:

在人工智能辅助的药物设计中,PMH为分子表示提供了一个强大的框架.
开发的矢量化方法提高了ML模型的准确性,用于预测结合亲和关系.
这项工作为推进计算药物发现和制药研究提供了宝贵的工具.

关键词:

持久的梅耶尔同源性持续的梅耶尔同源性机器学习是机器学习.蛋白质 - 配合物结合亲和力亲和力

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