  In this paper, we aim to synthesize cell microscopy images under different
molecular interventions, motivated by practical applications to drug
development. Building on the recent success of graph neural networks for
learning molecular embeddings and flow-based models for image generation, we
propose Mol2Image: a flow-based generative model for molecule to cell image
synthesis. To generate cell features at different resolutions and scale to
high-resolution images, we develop a novel multi-scale flow architecture based
on a Haar wavelet image pyramid. To maximize the mutual information between the
generated images and the molecular interventions, we devise a training strategy
based on contrastive learning. To evaluate our model, we propose a new set of
metrics for biological image generation that are robust, interpretable, and
relevant to practitioners. We show quantitatively that our method learns a
meaningful embedding of the molecular intervention, which is translated into an
image representation reflecting the biological effects of the intervention.
