论文标题
持续基于光谱的机器学习（Perspept ML）药物设计
Persistent spectral based machine learning (PerSpect ML) for drug design
论文作者
论文摘要
在本文中，我们建议用于药物设计的基于光谱的机器学习（Perspept ML）模型。持续的光谱模型，包括持续的光谱图，持续的光谱纯正复合物和持久的光谱超图，是基于光谱图理论，光谱简单复杂理论和光谱超图理论提出的。与以前所有光谱模型不同，持续同源性中提出的过滤过程被引入以生成多尺度光谱模型。更具体地说，从过滤过程中，可以系统地生成一系列嵌套的拓扑表示，即i，e。，图形，简单复合物和超图，并可以获得它们的光谱信息。持续的光谱变量定义为光谱变量在过滤值上的函数。从数学上讲，持续多样性（零特征值）正是持续的betti号码（或贝蒂曲线）。我们考虑11个持久的光谱变量，并将其用作蛋白质结合亲和力预测中机器学习模型的功能。我们在三个最常用的数据库中系统地测试了我们的模型，包括PDBBIND-2007，PDBBIND-2013和PDBBIND-2016。据我们所知，对于所有这些数据库，我们的结果比所有现有模型都要好。这证明了我们观点在分子数据分析和药物设计中的巨大力量。
In this paper, we propose persistent spectral based machine learning (PerSpect ML) models for drug design. Persistent spectral models, including persistent spectral graph, persistent spectral simplicial complex and persistent spectral hypergraph, are proposed based on spectral graph theory, spectral simplicial complex theory and spectral hypergraph theory, respectively. Different from all previous spectral models, a filtration process, as proposed in persistent homology, is introduced to generate multiscale spectral models. More specifically, from the filtration process, a series of nested topological representations, i,e., graphs, simplicial complexes, and hypergraphs, can be systematically generated and their spectral information can be obtained. Persistent spectral variables are defined as the function of spectral variables over the filtration value. Mathematically, persistent multiplicity (of zero eigenvalues) is exactly the persistent Betti number (or Betti curve). We consider 11 persistent spectral variables and use them as the feature for machine learning models in protein-ligand binding affinity prediction. We systematically test our models on three most commonly-used databases, including PDBbind-2007, PDBbind-2013 and PDBbind-2016. Our results, for all these databases, are better than all existing models, as far as we know. This demonstrates the great power of our PerSpect ML in molecular data analysis and drug design.
