基于Ollivier持续Ricci曲率（OPRC）的药物设计分子表示
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design
摘要：
有效的分子特征化是药物设计中机器学习模型的主要问题之一。在这里，我们提出了持久Ricci曲率（PRC），特别是奥利维尔持久Ricci曲率（OPRC），分子特征化和特征工程，第一次。利用持久同源性中提出的过滤过程生成一系列嵌套分子图。定义这些嵌套图上的Ollivier Ricci曲率的持久性和变化性为Ollivier持久Ricci曲率。此外，持久属性，这是在过滤过程中的OPRC的统计和组合特性，被用作分子描述符，并进一步与机器学习模型，特别是，梯度提升树（GBT）相结合。我们的OPRC-GBT模型用于预测蛋白质-配体结合亲和力，这是药物设计的关键步骤之一。基于来自完善的蛋白质-配体结合数据库的三个最常用的数据集，即，PDBbind，我们集中测试我们的模型，并与现有的模型进行比较。实验结果表明，该模型优于所有基于传统分子描述符的机器学习模型。
英文摘要：
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here we propose persistent Ricci curvature (PRC), in particular Ollivier persistent Ricci curvature (OPRC), for the molecular featurization and feature engineering, for the first time. Filtration process proposed in persistent homology is employed to generate a series of nested molecular graphs. Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as Ollivier persistent Ricci curvature. Moreover, persistent attributes, which are statistical and combinatorial properties of OPRCs during the filtration process, are used as molecular descriptors, and further combined with machine learning models, in particular, gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction of protein-ligand binding affinity, which is one of key steps in drug design. Based on three most-commonly used datasets from the well-established protein-ligand binding databank, i.e., PDBbind, we intensively test our model and compare with existing models. It has been found that our model are better than all machine learning models with traditional molecular descriptors.
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