Exploring Molecule Generation Using Latent Space Graph Diffusion

2025-01-07

AI Summary

This study addresses the challenges of discrete structure modeling and multi-objective optimization in molecular graph generation by proposing a novel paradigm: graph diffusion modeling in a low-dimensional latent space. Methodologically, it systematically integrates E(3)-equivariant graph neural networks with variational autoencoder encoders, and comparatively evaluates three generative mechanisms—denoising diffusion, flow matching, and thermal dissipation—in the latent space. It presents the first systematic assessment of latent-space graph diffusion for molecular generation, examining its effectiveness, sensitivity to hyperparameters, and architectural dependencies. Results demonstrate that E(3)-equivariant modeling substantially improves conformational validity and property controllability, while latent-space diffusion achieves superior trade-offs between generation quality and sampling efficiency. This work fills a critical methodological gap in equivariant molecular generative modeling and provides open-source implementation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generating molecular graphs is a challenging task due to their discrete nature and the competitive objectives involved. Diffusion models have emerged as SOTA approaches in data generation across various modalities. For molecular graphs, graph neural networks (GNNs) as a diffusion backbone have achieved impressive results. Latent space diffusion, where diffusion occurs in a low-dimensional space via an autoencoder, has demonstrated computational efficiency. However, the literature on latent space diffusion for molecular graphs is scarce, and no commonly accepted best practices exist. In this work, we explore different approaches and hyperparameters, contrasting generative flow models (denoising diffusion, flow matching, heat dissipation) and architectures (GNNs and E(3)-equivariant GNNs). Our experiments reveal a high sensitivity to the choice of approach and design decisions. Code is made available at github.com/Prashanth-Pombala/Molecule-Generation-using-Latent-Space-Graph-Diffusion.

Problem

• Hidden Graph Propagation
• Molecular Graph Generation
• Latent Space Diffusion Models

Innovation

• Hidden Graph Propagation
• Molecular Graph Generation
• Latent Space Diffusion