BioMatrix：迈向覆盖序列、结构和语言模态矩阵的综合性生物基础模型
BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language
AI总结：
提出首个原生多模态生物基础模型BioMatrix，通过统一分词方案将分子序列、结构、蛋白质序列、结构和自然语言映射到共享离散标记空间，在单一解码器架构下实现所有模态的统一生成，在80项任务中77项达到最优或竞争力水平。
摘要：
我们提出BioMatrix，这是首个在单一解码器架构中原生整合分子和蛋白质的序列、结构及自然语言的多模态基础模型。现有的生物基础模型分别追求原生多模态和广泛实体覆盖：那些在共享目标下融合多种模态的模型仍局限于单一实体类型，而覆盖多种实体类型的模型要么省略显式结构建模，要么依赖适配器设计，使得模型无法原生生成其可读取的模态。BioMatrix通过统一分词方案将分子序列（支持SMILES和SELFIES表示）、分子结构、蛋白质序列、蛋白质结构和自然语言映射到共享离散标记空间，从而在单一的下一个标记预测目标下统一消费和生成所有模态——无需外部编码器、投影适配器或模态特定输出头。基于Qwen3语言模型（1.7B和4B），BioMatrix在3044亿个标记上持续预训练，涵盖通用和领域特定文本、分子和蛋白质的序列与结构视图，以及将生物分子实体与科学文本交错并链接不同实体（通过分子-蛋白质和蛋白质-蛋白质相互作用数据）的跨模态语料。在涵盖6个类别80项任务（包括跨模态和模态内的单实体与多实体理解与生成任务）的全面下游应用套件上微调后，BioMatrix在80项任务中的77项上达到最优或竞争力性能，表明一个单一的原生多模态通才模型可以有效匹配或超越广泛生物任务中的专门方法。
英文摘要：
We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules and proteins within a single decoder-only architecture. Existing biological foundation models pursue native multimodality and broad entity coverage separately: those that fuse multiple modalities under a shared objective remain confined to a single entity type, while those spanning multiple entity types either omit explicit structural modeling or rely on adapter-based designs in which the model cannot natively generate the very modalities it can read. BioMatrix closes this gap by mapping molecular sequences (supporting both SMILES and SELFIES notations), molecular structures, protein sequences, protein structures, and natural language into a shared discrete token space through a unified tokenization scheme, so that all modalities are consumed and produced uniformly under a single next-token prediction objective -- without external encoders, projection adapters, or modality-specific output heads. Built upon the Qwen3 language model (1.7B and 4B), BioMatrix is continually pretrained on 304.4 billion tokens spanning general and domain-specific text, sequence and structure views of molecules and proteins, and cross-modal corpora that interleave biomolecular entities with scientific text and link distinct entities through molecule-protein and protein-protein interaction data. After tuning on a comprehensive suite of downstream applications covering 80 tasks across 6 categories -- encompassing single-entity and multi-entity understanding and generation tasks across and within modalities -- BioMatrix achieves state-of-the-art or competitive performance on 77 out of 80 tasks, demonstrating that a single, natively multimodal generalist model can effectively match or surpass specialized approaches across a wide range of biological tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
