BioMatrix：构建涵盖序列、结构和语言各维度的综合生物基础模型
BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language
摘要
我们介绍了BioMatrix——首个能够将序列、结构以及自然语言自然整合到单一解码器架构中的多模态基础模型。现有的生物基础模型通常分别实现多模态特性与多种实体的处理功能：那些将多种模态结合在同一目标下的模型，却只能处理单一类型的实体；而那些能够处理多种类型实体的模型，要么忽略结构建模，要么依赖适配器设计，而这样的设计使得模型无法自然生成其能够处理的各种模态信息。BioMatrix则通过统一的标记化方案，将分子序列（支持SMILES和SELFIES表示）、分子结构、蛋白质序列、蛋白质结构以及自然语言转化为同一个离散标记空间，从而让所有模态信息都能在相同的下一个标记预测目标下被统一处理——无需外部编码器、投影适配器或特定模态的输出头。BioMatrix基于Qwen3语言模型（1.7B和4B版本）构建，持续在304.4亿个标记上进行训练，这些标记涵盖了各种文本、序列和结构信息，以及将生物分子实体与科学文本结合的跨模态数据。经过针对6个类别中的80项任务的全面优化后，BioMatrix在77项任务中取得了最先进的或具有竞争力的性能，这表明单一的多模态通用模型能够有效替代或超越各种专业化的方法，适用于广泛的生物学任务。
English Abstract
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.