Topology-Driven Generative Completion of Lacunae in Molecular Data
This addresses the issue of incomplete molecular data for researchers in cheminformatics or drug discovery, but it appears incremental as it applies existing network completion methods to a specific domain.
The paper tackles the problem of targeted completion of lacunae (gaps) in molecular datasets by using topological data analysis, such as the Mapper algorithm, to guide scaffold-constrained generative models with various scoring functions, resulting in the addition of links and vertices to skeletonized data representations like Mapper graphs.
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.