hicai-zju/InstructBioMol-instruct-stage1

InstructBioMol: A Multimodal LLM for Biomolecule Understanding and Design

Paper • Project • Quickstart • Citation

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 and code repository.

Released Variants

Model Name	Stage	Multimodal	Description
InstructBioMol-base	Pretraining	❎	Continual pretrained model on molecular sequences, protein sequences, and scientific literature.
InstructBioMol-instruct-stage1 (This Model)	Instruction tuning (stage 1)	✅	Stage1 instruction-tuned model with biomolecular multimodal processing capabilities. (e.g., 3D molecules/proteins)
InstructBioMol-instruct	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-base

Training Data:

​1. Molecule - Natural Language Alignment:

• 60 million data from pubchem and chebi

​2. Protein - Natural Langauge Alignment:

• 35 million data from UniProt (Swiss-Prot and TrEMBL)

​3. Molecule - Protein Alignment:

• 1 million data from BindingDB and Rhea

Training Objective: Instruction tuning

Citation

@article{DBLP:journals/corr/abs-2410-07919,
  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        = {InstructBioMol: Advancing Biomolecule Understanding and Design Following
                  Human Instructions},
  journal      = {CoRR},
  volume       = {abs/2410.07919},
  year         = {2024}
}