Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design

Abstract

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 proteinligand 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.

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

“…Xia et al propose a persistent graph curvature descriptor to characterize molecular features based on the observation that biomolecules have a hierarchical and multiscale structure [27,43]. They first filter the edges of the graph by length to remove short edges that are less relevant to the hierarchical structure, and then construct a sequence of subgraphs, where each subgraph is a subset of the next one.…”

“…Biomolecules often have hierarchical and multiscale structures, requiring a multiscale Ricci curvature to accurately characterize these structures and interactions. Such curvature has been proposed in [27,43]. However, their curvature descriptor for protein-ligand complexes relies on prior knowledge such as the average and variance of all curvatures, and therefore, is not universally applicable.…”

“…Based on this generalization, two different curvature-based graph neural networks [25,26] have been proposed, and they perform well on baseline datasets. Biomolecules often exhibit hierarchical and multiscale structures, which require a multiscale representation to accurately characterize their interactions [27]. It implies multiscale curvature for graph is more suitable.…”

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

“…Persistent Graph-Curvature-descriptors Xia et al propose a persistent graph curvature descriptor to characterize molecular features based on the observation that biomolecules have a hierarchical and multiscale structure [27,43]. They first filter the edges of the graph by length to remove short edges that are less relevant to the hierarchical structure, and then construct a sequence of subgraphs, where each subgraph is a subset of the next one.…”