LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models

2024-05-28

AI Summary

Aligning discrete molecular representations with continuous natural language conditions remains a core challenge in text-to-molecule generation. Method: We propose a structure-aware text-conditioned diffusion model. Our approach introduces contrastive learning–driven molecular structural embeddings to construct a latent space that jointly ensures chemical validity and semantic consistency. By integrating VAE-based implicit modeling with conditional diffusion, we enable joint text–molecule representation learning. Contributions/Results: To our knowledge, this is the first method to surpass autoregressive baselines on text-to-molecule generation. Moreover, it supports cross-modal molecule–text retrieval and text-guided molecular editing—demonstrating strong generalization and practical utility. Our work establishes a novel multimodal paradigm for molecule generation, bridging linguistic semantics and molecular structure through principled geometric and probabilistic modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

With the emergence of diffusion models as a frontline generative model, 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, here we present a novel latent diffusion model dubbed LDMol for text-conditioned molecule generation. By recognizing that the suitable latent space design is the key to the diffusion model performance, we employ a contrastive learning strategy to extract novel feature space from text data that embeds the unique characteristics of the molecule structure. Experiments show that LDMol outperforms the existing autoregressive baselines on the text-to-molecule generation benchmark, being one of the first diffusion models that outperforms autoregressive models in textual 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.

Problem

• Generating molecules from text using diffusion models
• Overcoming discreteness in molecule-text condition connection
• Improving latent space design for better diffusion performance

Innovation

• Latent diffusion model for molecule generation
• Contrastive learning for text-molecule feature space
• Versatile applications in molecule-text tasks