一个模型看懂分子、蛋白质和语言
现在的生物AI模型要么只能处理一种分子（比如蛋白质），要么能处理多种但需要外挂不同的“翻译器”，没法自己生成结构。BioMatrix把分子序列、分子结构、蛋白质序列、蛋白质结构和自然语言全部转成同一种“语言”，让一个模型既能读也能写，不需要额外插件。它在80个任务上赢了77个，包括理解分子、预测相互作用、生成结构等。虽然它不是你明天就能直接用的工具，但它展示了“一个通用模型搞定所有生物数据”是可行的，未来可能让药物设计、蛋白质工程等领域的AI工具更统一、更强大。
📄 原文摘要(英文)
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.