用少量分子-评分示例引导扩散模型，实现高效分子设计。
Graph Diffusion Transformers are In-Context Molecular Designers
- 用分子-评分示例定义任务上下文，指导生成目标分子。
- 在33个任务中平均排名3.63，优于大100-1000倍的语言模型。
- 适用于药物与材料设计，适合需要快速适配新任务的研究者。
上下文学习使大模型能从少量示例中适应新任务，但在分子设计中表现有限。现有数据库如ChEMBL包含数百万生物检测的分子属性，但每项属性的标注数据仍稀少。为此，我们提出演示条件扩散模型（DemoDiff），以少量分子-评分示例代替文本描述定义任务上下文，引导去噪Transformer生成符合目标属性的分子。为支持可扩展预训练，我们设计了基于节点对编码（Node Pair Encoding）的新分子分词器，可在片段层面表示分子，节点数减少5.5倍。我们构建了一个涵盖药物与材料的百万级上下文任务数据集，并在该数据集上预训练了一个0.7亿参数模型。在六类共33个设计任务中，DemoDiff性能匹配或超越参数量大100-1000倍的语言模型，平均排名3.63，显著优于领域专用方法的5.25-10.20。结果表明，DemoDiff是面向上下文分子设计的分子基础模型。代码已开源。
原文摘要 · Abstract (English)
In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL contain molecular properties spanning millions of biological assays, yet labeled data for each property remain scarce. To address this limitation, we introduce demonstration-conditioned diffusion models (DemoDiff), which define task contexts using a small set of molecule-score examples instead of text descriptions. These demonstrations guide a denoising Transformer to generate molecules aligned with target properties. For scalable pretraining, we develop a new molecular tokenizer with Node Pair Encoding that represents molecules at the motif level, requiring 5.5$\times$ fewer nodes. We curate a dataset containing millions of context tasks from multiple sources covering both drugs and materials, and pretrain a 0.7-billion-parameter model on it. Across 33 design tasks in six categories, DemoDiff matches or surpasses language models 100-1000$\times$ larger and achieves an average rank of 3.63 compared to 5.25-10.20 for domain-specific approaches. These results position DemoDiff as a molecular foundation model for in-context molecular design. Our code is available at https://github.com/liugangcode/DemoDiff.
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