  Recently, machine learning methods have been used to propose molecules with
desired properties, which is especially useful for exploring large chemical
spaces efficiently. However, these methods rely on fully labelled training
data, and are not practical in situations where molecules with multiple
property constraints are required. There is often insufficient training data
for all those properties from publicly available databases, especially when
ab-initio simulation or experimental property data is also desired for training
the conditional molecular generative model. In this work, we show how to modify
a semi-supervised variational auto-encoder (SSVAE) model which only works with
fully labelled and fully unlabelled molecular property training data into the
ConGen model, which also works on training data that have sparsely populated
labels. We evaluate ConGen's performance in generating molecules with multiple
constraints when trained on a dataset combined from multiple publicly available
molecule property databases, and demonstrate an example application of building
the virtual chemical space for potential Lithium-ion battery localized
high-concentration electrolyte (LHCE) diluents.
