  With the emergence of diffusion models as the frontline of generative models,
many researchers have proposed molecule generation techniques using conditional
diffusion models. However, due to the fundamental nature of a molecule, which
carries highly entangled correlations within a small number of atoms and bonds,
it becomes difficult for a model to connect raw data with the conditions when
the conditions become more complex as natural language. To address this, here
we present a novel latent diffusion model dubbed LDMol, which enables a natural
text-conditioned molecule generation. Specifically, LDMol is composed of three
building blocks: a molecule encoder that produces a chemically informative
feature space, a natural language-conditioned latent diffusion model using a
Diffusion Transformer (DiT), and an autoregressive decoder for molecule re. In
particular, recognizing that multiple SMILES notations can represent the same
molecule, we employ a contrastive learning strategy to extract the chemical
informative feature space. LDMol not only beats the existing baselines on the
text-to-molecule generation benchmark but is also capable of zero-shot
inference with unseen scenarios. Furthermore, we show that LDMol can be applied
to downstream tasks such as molecule-to-text retrieval and text-driven molecule
editing, demonstrating its versatility as a diffusion model.
