  Recent advances in artificial intelligence have propelled the development of
innovative computational materials modeling and design techniques. Generative
deep learning models have been used for molecular representation, discovery,
and design. In this work, we assess the predictive capabilities of a molecular
generative model developed based on variational inference and graph theory in
the small data regime. Physical constraints that encourage energetically stable
molecules are proposed. The encoding network is based on the scattering
transform with adaptive spectral filters to allow for better generalization of
the model. The decoding network is a one-shot graph generative model that
conditions atom types on molecular topology. A Bayesian formalism is considered
to capture uncertainties in the predictive estimates of molecular properties.
The model's performance is evaluated by generating molecules with desired
target properties.
