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Efficient molecular featurization is one of the major issues for machinelearning models in drug design. Here we propose persistent Ricci curvature(PRC), in particular Ollivier persistent Ricci curvature (OPRC), for themolecular featurization and feature engineering, for the first time. Filtrationprocess proposed in persistent homology is employed to generate a series ofnested molecular graphs. Persistence and variation of Ollivier Ricci curvatureson these nested graphs are defined as Ollivier persistent Ricci curvature.Moreover, persistent attributes, which are statistical and combinatorialproperties of OPRCs during the filtration process, are used as moleculardescriptors, and further combined with machine learning models, in particular,gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction ofprotein-ligand binding affinity, which is one of key steps in drug design.Based on three most-commonly used datasets from the well-establishedprotein-ligand binding databank, i.e., PDBbind, we intensively test our modeland compare with existing models. It has been found that our model are betterthan all machine learning models with traditional molecular descriptors.

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