Fraglingo：基于连接感知自回归片段生成的分子设计
Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation
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中文总结 AI 辅助
Fraglingo提出一种连接感知的自回归片段生成方法，在连续潜在空间联合建模片段与连接，实现分子生成、骨架修饰和性质优化，并支持无需重训的片段库扩展。
中文摘要 AI 辅助
当生成过程能够模拟化学家实际进行的编辑操作时，分子设计最为有效：扩展骨架、替换取代基，或在指定连接位点装饰骨架，同时优化分子性质。基于片段的分子设计天然支持这种工作流程，然而现有方法往往将片段选择与连接预测分离，即先从固定词汇表中选取一个片段，再预测其应如何连接。这种解耦方式将生成限制在封闭的片段词汇表内，并将连接视为一个独立的预测问题。我们提出Fraglingo，一种自回归的基于片段的分子生成器，它在连续潜在空间中联合建模片段身份与连接。Fraglingo预测一个连接感知的片段嵌入，并通过潜在空间最近邻搜索检索下一个片段。为了编码连接上下文，我们引入了一种通配符锚定的读出机制，该机制从活动连接位点的角度表示增长中的分子，使预测的嵌入能够同时捕获分子上下文和所需的连接。由于生成过程在连续嵌入空间而非固定片段标识符上进行，因此可以在不重新训练的情况下向推理时词汇表添加新片段，前提是这些片段的嵌入可由训练好的片段编码器计算得出。这种基于检索的公式为分子生成、骨架生成、骨架装饰和分子优化提供了统一的生成原语。在受控性质条件基准测试中，Fraglingo在联合性质控制方面优于同等训练的基线模型，同时保持了有竞争力的有效性、唯一性和新颖性。此外，Fraglingo能够泛化到比训练时所用片段库大4倍的片段库，而无需重新训练。
英文摘要
We introduce Fraglingo, an autoregressive molecular generator that constructs molecules step by step from chemically meaningful fragments connected through predefined attachment sites. At each generation step, Fraglingo jointly predicts which fragment to add and how it should attach by producing an attachment-aware fragment embedding and retrieving the nearest fragment through latent-space search. A wildcard-anchored readout represents both the growing molecule and candidate fragments relative to their attachment sites, enabling a single latent prediction to determine both fragment identity and attachment configuration. Because prediction operates in a continuous embedding space rather than over fixed fragment identifiers, larger fragment libraries can be introduced at inference time without retraining. This retrieval-based formulation provides a unified generation primitive for molecule generation, scaffold generation, scaffold decoration, and molecule optimization. Fraglingo also supports property-conditional generation, allowing desired molecular properties to guide the generation process. On controlled property-conditional benchmarks, Fraglingo achieves stronger joint property control than comparably trained baselines while maintaining competitive validity, uniqueness, and novelty. It also generalizes to fragment libraries up to four times larger than those used during training without retraining. Code is available at: https://anonymous.4open.science/r/FragLingo-3551.
发表机构
- Siebel School of Computing and Data Science（西贝尔计算与数据科学学院）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
