This repository contains the DemoDiff model, a diffusion-based molecular foundation model for in-context inverse molecular design, as presented in the paper Graph Diffusion Transformers are In-Context Molecular Designers.

DemoDiff leverages graph diffusion transformers to generate molecules based on contextual examples, enabling few-shot molecular design across diverse chemical tasks without task-specific fine-tuning. It introduces demonstration-conditioned diffusion models, which define task contexts using a small set of molecule-score examples instead of text descriptions to guide a denoising Transformer for molecule generation. A novel molecular tokenizer with Node Pair Encoding is developed for scalable pretraining, representing molecules at the motif level.

Code: https://github.com/liugangcode/DemoDiff