Graph Diffusion Transformers for Multi-Conditional Molecular Generation

TL;DR

This paper introduces Graph Diffusion Transformer (Graph DiT), a novel model for multi-conditional molecular generation that effectively incorporates multiple property constraints and outperforms previous methods across various metrics.

Contribution

The paper presents a new graph-dependent noise model and a Transformer-based denoiser for improved multi-conditional molecular generation.

Findings

Graph DiT outperforms existing models on nine metrics.

Effective integration of multiple property constraints.

Practical utility demonstrated in polymer inverse design.

Abstract

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecular generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT integrates an encoder to learn numerical and categorical property representations with the Transformer-based denoiser. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, Graph DiT is trained with a novel graph-dependent noise model for accurate estimation of graph-related noise in molecules. We extensively validate Graph DiT for multi-conditional polymer and small molecule generation. Results demonstrate the superiority of Graph DiT across nine metrics from distribution learning to condition control for molecular properties. A polymer inverse design task for gas separation with feedback from domain experts further demonstrates its practical utility.