| datasets: | |
| - liuganghuggingface/demodiff_downstream | |
| license: mit | |
| tags: | |
| - chemistry | |
| - biology | |
| pipeline_tag: graph-ml | |
| # DemoDiff: Graph Diffusion Transformers are In-Context Molecular Designers | |
| 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](https://huggingface.co/papers/2510.08744). | |
| 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 | |
| ## 🌟 Key Features | |
| - **In-Context Learning**: Generate molecules using only contextual examples (no fine-tuning required) | |
| - **Graph-Based Tokenization**: Novel molecular graph tokenization with BPE-style vocabulary | |
| - **Comprehensive Benchmarks**: 30+ downstream tasks covering drug discovery, docking, and polymer design | |
| ### Model Configuration | |
| | Parameter | Value | Description | | |
| |------------|--------|-------------| | |
| | **context_length** | 150 | Maximum sequence length for the input context. | | |
| | **depth** | 24 | Number of transformer layers. | | |
| | **diffusion_steps** | 500 | Number of diffusion steps during training. | | |
| | **hidden_size** | 1280 | Hidden dimension size in the transformer. | | |
| | **mlp_ratio** | 4 | Expansion ratio in the MLP block. | | |
| | **num_heads** | 16 | Number of attention heads. | | |
| | **task_name** | `pretrain` | Task type for model training. | | |
| | **tokenizer_name** | `pretrain` | Tokenizer used for model input. | | |
| | **vocab_ring_len** | 300 | Length of the circular vocabulary window. | | |
| | **vocab_size** | 3000 | Total vocabulary size. | |