D3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation
This work addresses the problem of generating biologically or material-relevant molecules for applications in chemistry, material science, and drug development, representing a novel method for a known bottleneck.
The paper tackles the challenge of generating complex 3D molecular structures by introducing a diffusion model that combines a Diffusion Transformer with multihead equivariant self-attention, achieving state-of-the-art performance across several key metrics.
Understanding and predicting the diverse conformational states of molecules is crucial for advancing fields such as chemistry, material science, and drug development. Despite significant progress in generative models, accurately generating complex and biologically or material-relevant molecular structures remains a major challenge. In this work, we introduce a diffusion model for three-dimensional (3D) molecule generation that combines a classifiable diffusion model, Diffusion Transformer, with multihead equivariant self-attention. This method addresses two key challenges: correctly attaching hydrogen atoms in generated molecules through learning representations of molecules after hydrogen atoms are removed; and overcoming the limitations of existing models that cannot generate molecules across multiple classes simultaneously. The experimental results demonstrate that our model not only achieves state-of-the-art performance across several key metrics but also exhibits robustness and versatility, making it highly suitable for early-stage large-scale generation processes in molecular design, followed by validation and further screening to obtain molecules with specific properties.