  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.
