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.

LDMol 是一种新颖的潜在扩散模型，能够实现自然语言条件下的分子生成，并通过对比学习策略来提取化学信息特征空间，不仅在文本到分子生成基准测试中超过现有基准，还能进行零样本推理和相关任务如分子到文本检索和以文本为驱动的分子编辑，展示了它作为扩散模型的通用性。