LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space
<|reference_start|>LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space: With the emergence of diffusion models as the frontline of generative models, many researchers have proposed molecule generation techniques with conditional diffusion models. However, the unavoidable discreteness of a molecule makes it difficult for a diffusion model to connect raw data with highly complex conditions like natural language. To address this, we present a novel latent diffusion model dubbed LDMol for text-conditioned molecule generation. LDMol comprises a molecule autoencoder that produces a learnable and structurally informative feature space, and a natural language-conditioned latent diffusion model. In particular, recognizing that multiple SMILES notations can represent the same molecule, we employ a contrastive learning strategy to extract feature space that is aware of the unique characteristics of the molecule structure. LDMol outperforms the existing baselines on the text-to-molecule generation benchmark, suggesting a potential for diffusion models can outperform autoregressive models in text data generation with a better choice of the latent domain. Furthermore, we show that LDMol can be applied to downstream tasks such as molecule-to-text retrieval and text-guided molecule editing, demonstrating its versatility as a diffusion model.<|reference_end|>
@article{chang2024ldmol:,
    title={LDMol: Text-to-Molecule Diffusion Model with Structurally Informative
  Latent Space},
    author={Jinho Chang and Jong Chul Ye},
    journal={arXiv preprint arXiv:2405.17829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17829},
    primaryClass={cs.LG cs.AI}
}