--- license: mit ---

InstructBioMol: A Multimodal LLM for Biomolecule Understanding and Design

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### Model Description InstructBioMol is a multimodal large language model that bridges natural language with biomolecules (proteins and small molecules). It achieves any-to-any alignment between natural language, molecules, and proteins through comprehensive instruction tuning. *For detailed information, please refer to our [paper](https://arxiv.org/abs/2410.07919) and [code repository](https://github.com/HICAI-ZJU/InstructBioMol).* ### Released Variants | Model Name | Stage | Multimodal| Description | |------------|-----------| -------| -------| | [InstructBioMol-base](https://huggingface.co/hicai-zju/InstructBioMol-base) | Pretraining | ❎| Continual pretrained model on molecular sequences, protein sequences, and scientific literature. | | [InstructBioMol-instruct-stage1](https://huggingface.co/hicai-zju/InstructBioMol-instruct-stage1) | Instruction tuning (stage 1) | ✅ | Stage1 instruction-tuned model with biomolecular multimodal processing capabilities. (e.g., 3D molecules/proteins) | | [InstructBioMol-instruct](https://huggingface.co/hicai-zju/InstructBioMol-instruct) (*This Model*) | Instruction tuning (stage 1 and 2) | ✅| Fully instruction-tuned model (stage1 & stage2) with biomolecular multimodal processing capabilities (e.g., 3D molecules/proteins) | ### Training Details **Base Architecture**: InstructBioMol-instruct-stage1 **Training Data**: ​1. Molecule - Natural Language Alignment: - 52K data from chebi ​2. Protein - Natural Langauge Alignment: - 2 million data from UniProt (Swiss-Prot) ​3. Molecule - Protein Alignment: - 1 million data from BindingDB and Rhea **Training Objective**: Instruction tuning ### Citation ```bibtex @article{zhuang2025advancing, author = {Xiang Zhuang and Keyan Ding and Tianwen Lyu and Yinuo Jiang and Xiaotong Li and Zhuoyi Xiang and Zeyuan Wang and Ming Qin and Kehua Feng and Jike Wang and Qiang Zhang and Huajun Chen}, title={Advancing biomolecular understanding and design following human instructions}, journal={Nature Machine Intelligence}, pages={1--14}, year={2025}, publisher={Nature Publishing Group UK London} } ```