Graph Diffusion Transformer for Multi-Conditional Molecular Generation

arXiv:2401.13858v2 Announce Type: replace
Abstract: Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule 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 has a condition encoder to learn the representation of numerical and categorical properties and utilizes a Transformer-based graph denoiser to achieve …

abstract arxiv constraints cs.lg design diffusion diffusion models diffusion transformer discovery drug discovery graph material multiple q-bio.bm success synthetic the graph transformer type