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3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation (2403.07179v2)

Published 11 Mar 2024 in cs.LG, cs.CL, and q-bio.BM

Abstract: Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally novel molecular structures with desired properties. 3M-Diffusion encodes molecular graphs into a graph latent space which it then aligns with the text space learned by encoder-based LLMs from textual descriptions. It then reconstructs the molecular structure and atomic attributes based on the given text descriptions using the molecule decoder. It then learns a probabilistic mapping from the text space to the latent molecular graph space using a diffusion model. The results of our extensive experiments on several datasets demonstrate that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided.

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Overview of 3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs

The paper "3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs" by Huaisheng Zhu, Teng Xiao, and Vasant G. Honavar introduces 3M-Diffusion, a novel multi-modal molecular graph generation method designed to generate molecular structures from textual descriptions. This approach addresses significant limitations in existing molecule generation methodologies, particularly in achieving diversity, novelty, and quality in the generated molecules while maintaining semantic coherence with the input text.

Methodology

The 3M-Diffusion framework integrates a multi-modal alignment of molecular graphs and textual descriptions within a diffusion model. The model consists of two main components: a text-molecule aligned variational autoencoder (VAE) and a multi-modal molecule latent diffusion model. The former encodes molecular graphs into a graph latent space aligned with textual descriptions through contrastive learning. The latter learns a probabilistic mapping from the text space to the molecular graph latent space using a conditional diffusion model.

Key Components:

  1. Text-Molecule Aligned Variational Autoencoder:
    • Molecular Graph Encoder: Employs Graph Isomorphism Networks (GIN) to encode molecular structures into continuous latent spaces.
    • Text Encoder: Utilizes Sci-BERT to map textual descriptions into latent spaces, leveraging pretrained transformer models for scientific text.
    • Representation Alignment: Uses contrastive learning to align the latent representations of molecular graphs and textual descriptions.
    • Molecular Graph Decoder: Hierarchical Variational Autoencoder (HierVAE) is used to reconstruct molecular graphs from the latent space.
  2. Multi-Modal Molecule Latent Diffusion:
    • Denoising Network: Trained to denoise noisy latent representations conditioned on the text, enhancing the generation of high-quality molecular graphs.
    • Classifier-Free Guidance: Improves generated sample quality by combining conditional and unconditional sampling during inference.

Experimental Results

Experiments were conducted on four datasets: PubChem, ChEBI-20, PCDes, and MoMu. The performance of 3M-Diffusion was compared against state-of-the-art text-to-molecule models such as MolT5 and ChemT5. The evaluation metrics included Similarity, Novelty, Diversity, and Validity of the generated molecules.

Notable Findings:

  • 3M-Diffusion significantly outperformed MolT5 and ChemT5 in terms of diversity and novelty while maintaining high similarity with the target descriptions.
  • The model demonstrated strong numerical results with a relative improvement in novelty (146.27% on PCDes) and diversity (130.04% on PCDes) over the best-performing baseline.
  • The generated molecules exhibited higher semantic coherence with the textual descriptions and better properties, such as higher logP values for certain prompts indicating improved solubility characteristics.

Implications and Future Directions

The implications of 3M-Diffusion span both theoretical and practical realms:

Theoretical:

  • The introduction of the contrastive-learning-based alignment between text and molecular graph latent spaces addresses a critical gap in existing generative models, which often fail to map high-dimensional text and graph representations effectively.
  • The integration of latent diffusion models with multi-modal data represents a compelling advancement in generative model architectures, offering a robust framework adaptable to other text-graph generative tasks.

Practical:

  • The ability to generate diverse and novel molecular structures from textual descriptions can significantly accelerate drug discovery and materials science by enabling rapid prototyping of candidate molecules.
  • The improved sampling efficiency and quality of generated molecules have potential applications in automating the initial stages of drug design and materials synthesis pipelines.

Speculative Future Developments:

  • Future enhancements could explore extending the model to include 3D molecular conformations, broadening its applicability to more complex molecular design tasks.
  • Incorporating experimental feedback loops where generated molecules are synthesized and tested in laboratory settings could further refine and validate the model's practical utility.
  • The methodology could be adapted to other domains requiring cross-modal generative models, such as protein-folding prediction, chemical reaction generation, and beyond.

In conclusion, 3M-Diffusion represents a significant advancement in the intersection of natural language processing and molecular graph generation, setting a new benchmark for text-guided molecular generation tasks. The promising results showcased in this paper highlight the potential of multi-modal diffusion models to revolutionize the field of computational chemistry and materials science.

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Authors (3)
  1. Huaisheng Zhu (13 papers)
  2. Teng Xiao (40 papers)
  3. Vasant G Honavar (11 papers)
Citations (1)
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