  Can we train a molecule generator that can generate 3D molecules from a new
domain, circumventing the need to collect data? This problem can be cast as the
problem of domain adaptive molecule generation. This work presents a novel and
principled diffusion-based approach, called GADM, that allows shifting a
generative model to desired new domains without the need to collect even a
single molecule. As the domain shift is typically caused by the structure
variations of molecules, e.g., scaffold variations, we leverage a designated
equivariant masked autoencoder (MAE) along with various masking strategies to
capture the structural-grained representations of the in-domain varieties. In
particular, with an asymmetric encoder-decoder module, the MAE can generalize
to unseen structure variations from the target domains. These structure
variations are encoded with an equivariant encoder and treated as domain
supervisors to control denoising. We show that, with these encoded
structural-grained domain supervisors, GADM can generate effective molecules
within the desired new domains. We conduct extensive experiments across various
domain adaptation tasks over benchmarking datasets. We show that our approach
can improve up to 65.6% in terms of success rate defined based on molecular
validity, uniqueness, and novelty compared to alternative baselines.
