Topology-Driven Generative Completion of Lacunae in Molecular Data

TL;DR

This paper presents a novel topology-driven method for completing missing parts in molecular data sets by leveraging topological data analysis and generative models, demonstrated on patent-derived molecular graphs.

Contribution

It introduces a new approach combining topological data analysis with scaffold-constrained generative models for targeted molecular data completion.

Findings

Successfully repaired lacunae in molecular datasets

Enhanced data set completeness using topology-based methods

Demonstrated application on patent-derived molecular graphs

Abstract

We introduce an approach to the targeted completion of lacunae in molecular data sets which is driven by topological data analysis, such as Mapper algorithm. Lacunae are filled in using scaffold-constrained generative models trained with different scoring functions. The approach enables addition of links and vertices to the skeletonized representations of the data, such as Mapper graph, and falls in the broad category of network completion methods. We illustrate application of the topology-driven data completion strategy by creating a lacuna in the data set of onium cations extracted from USPTO patents, and repairing it.