Graph Diffusion Transformers for Multi-Conditional Molecular Generation
This work addresses the challenge of multi-conditional molecular generation for material and drug discovery, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of generating molecules with multiple desired properties, such as synthetic score and gas permeability, by introducing Graph Diffusion Transformer (Graph DiT), which achieves superior performance across nine metrics for polymer and small molecule generation, including practical validation in a gas separation task.
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.