标题： FragmentNet：用于图到序列分子表示学习的自适应图碎片化 显示英文标题

标题： FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning

摘要： 分子属性预测利用分子结构来推断化学性质。能够捕捉有意义的分子内相互作用的化学可解释表示可以提高这些预测的可用性和效果。然而，现有方法通常依赖于基于原子或规则的片段分词，这可能在化学上不是最优的，并且缺乏可扩展性。我们引入了FragmentNet，这是一种图到序列的基础模型，具有自适应的、学习到的分词器，能够在保持结构连通性的同时将分子图分解为化学有效的片段。FragmentNet集成了VQVAE-GCN用于层次化片段嵌入，空间位置编码用于图序列化，全局分子描述符和变压器。通过掩码片段建模进行预训练，并在MoleculeNet任务上进行微调，FragmentNet在具有类似规模架构和数据集的模型中表现更好，并且与需要显著更多资源的更大最先进的模型相媲美。这种新框架实现了分子图的自适应分解、序列化和重建，促进了基于片段的编辑和学习嵌入中的属性趋势可视化——这是分子设计和优化的强大工具。 显示英文摘要

摘要： Molecular property prediction uses molecular structure to infer chemical properties. Chemically interpretable representations that capture meaningful intramolecular interactions enhance the usability and effectiveness of these predictions. However, existing methods often rely on atom-based or rule-based fragment tokenization, which can be chemically suboptimal and lack scalability. We introduce FragmentNet, a graph-to-sequence foundation model with an adaptive, learned tokenizer that decomposes molecular graphs into chemically valid fragments while preserving structural connectivity. FragmentNet integrates VQVAE-GCN for hierarchical fragment embeddings, spatial positional encodings for graph serialization, global molecular descriptors, and a transformer. Pre-trained with Masked Fragment Modeling and fine-tuned on MoleculeNet tasks, FragmentNet outperforms models with similarly scaled architectures and datasets while rivaling larger state-of-the-art models requiring significantly more resources. This novel framework enables adaptive decomposition, serialization, and reconstruction of molecular graphs, facilitating fragment-based editing and visualization of property trends in learned embeddings - a powerful tool for molecular design and optimization.