BioMatrix-1.7B-SFT/README.md

---
license: apache-2.0
language:
- en
tags:
- biology
- chemistry
- molecule
- protein
- multimodal
- foundation-model
- drug-discovery
- protein-design
pipeline_tag: text-generation
base_model: QizhiPei/BioMatrix-1.7B-Base
library_name: transformers
---

# BioMatrix-1.7B-SFT

<div align="center">
  <img src="figures/biomatrix_logo.png" width="60%" alt="BioMatrix" />
</div>

**BioMatrix** is a multimodal biological foundation model that natively integrates **1D sequences**, **3D structures**, and **natural language** for both **molecules** and **proteins** within a single decoder-only architecture.

This is the **1.7B-parameter SFT (Supervised Fine-Tuned)** variant, instruction-tuned across 80 downstream biological tasks spanning 6 categories. For a larger and more capable model, see [BioMatrix-4B-SFT](https://huggingface.co/QizhiPei/BioMatrix-4B-SFT).

- 📄 **Paper**: [BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language](http://arxiv.org/abs/2606.22138)
- 💻 **Code**: [https://github.com/QizhiPei/BioMatrix](https://github.com/QizhiPei/BioMatrix)
- 🤗 **Model & Data Collection**: [https://huggingface.co/collections/QizhiPei/biomatrix](https://huggingface.co/collections/QizhiPei/biomatrix)

<p align="center">
  <img width="90%" src="figures/biomatrix_arch_v1.png" alt="BioMatrix Architecture">
</p>

## Model Overview

BioMatrix closes the gap between native multimodality and broad entity coverage in biological foundation models. Unlike adapter-based approaches that bolt external encoders onto a language model, or prior native-tokenization models confined to a single entity type, BioMatrix maps **all modalities into a shared discrete token space** via a unified tokenization scheme:

- **Molecular 1D sequences** (both SMILES and SELFIES notations)
- **Molecular 3D structures** (via MolStrucTok with branch-decoupled decoder)
- **Protein 1D sequences** (residue-level tokens)
- **Protein 3D structures** (via GCP-VQVAE backbone tokenizer)
- **Natural language** (inherited from Qwen3 tokenizer)

All modalities are consumed and produced uniformly under a **single next-token prediction objective**—without external encoders, projection adapters, or modality-specific output heads.

| Model | Molecule 1D | Molecule 3D | Protein 1D | Protein 3D | Natural Language |
|-------|:-----------:|:-----------:|:----------:|:----------:|:----------------:|
| ESM3 | ✗ | ✗ | ✓ | ✓ | ✓ |
| 3D-MoLM | ✓ | ✓ | ✗ | ✗ | ✓ |
| AlphaFold3 | ✓ | ✓ | ✓ | ✓ | ✗ |
| BioT5/BioT5+ | ✓ | ✗ | ✓ | ✗ | ✓ |
| BioMedGPT | ✓ | ✗ | ✓ | ✗ | ✓ |
| NatureLM | ✓ | ✗ | ✓ | ✗ | ✓ |
| SciReasoner | ✓ | ✗ | ✓ | ✗ | ✓ |
| **BioMatrix** | **✓** | **✓** | **✓** | **✓** | **✓** |

<p align="center">
  <img width="95%" src="figures/3D_tokenizers.png" alt="Molecule and Protein Structure Tokenizers">
</p>

## Model Details

- **Base Architecture**: Qwen3-1.7B-Base
- **Parameters**: 1.7B
- **Training Stages**:
  - **Continual Pretraining** on 304.4B tokens (general/scientific text, molecular & protein 1D/3D data, cross-modal interleaved corpora)
  - **Instruction Tuning** on a comprehensive suite of 80 downstream tasks across 6 categories
- **Context Length**: 8,192 tokens
- **Tokenizer**: Extended Qwen3 vocabulary with:
  - 11,294 joint molecular 3D tokens (composed from SELFIES atom × MolStrucTok codes)
  - 4,096 protein 3D tokens (GCP-VQVAE codebook)
  - 26 protein 1D tokens (amino acids + non-standard/unknown)
  - SELFIES atom tokens and modality-specific control tokens

## Pretraining Corpus (304.4B tokens)

| Category | Tokens | Sources |
|----------|--------|---------|
| **Text** | 105.3B | FineWeb-Edu, FineFineWeb (biology/chemistry/medical/health), PubMed Full Articles |
| **Molecule** | 73.7B | PubChem, PCQM4Mv2, PubChemQC, MolTextNet |
| **Protein** | 77.4B | UniRef50, RCSB PDB, Swiss-Prot, TrEMBL, AlphaFold DB |
| **Cross-entity** | 48.0B | Interleaved text (PubMed, bioRxiv, S2ORC, USPTO), Molecule–protein (BindingDB, STITCH, jglaser, CrossDocked), Protein–protein (AlphaSeq, PPIRef) |

<p align="center">
  <img width="95%" src="figures/biomatrix_cpt_data_v5.png" alt="BioMatrix Continual Pretraining Data">
</p>

## Performance Highlights

Despite its compact 1.7B size, BioMatrix delivers strong performance across diverse biological tasks—often surpassing models several times larger. Selected highlights:

### Molecular Tasks
- **Unconditional 1D Generation** (GuacaMol, SELFIES): 0.999 validity, 1.000 uniqueness
- **Name Conversion (I2S EM)**: 87.22% (surpasses SciReasoner-8B at 84.40%)
- **Text-Based Molecule Generation (EM)**: 56.35% (vs. SciReasoner-8B: 48.00%)
- **MoleculeQA Total**: 70.07% (vs. prior best MolCA-1.3B: 64.79%)
- **Property-Conditioned 3D Generation**: ~3-4× error reduction on QM9 electronic-structure targets

### Protein Tasks
- **Fold Type Prediction (Family level)**: 85.84% accuracy
- **EC Number Prediction (Price split, F1)**: 34.34% (surpasses SciReasoner-8B at 22.00%)
- **Inverse Folding AAR**: 75.20% (vs. DPLM-2-3B: 61.67%)
- **Sequence–Structure Co-generation**: scTM = 0.965, scRMSD = 2.81

### Interaction Tasks
- **BindingDB Affinity (RMSE)**: 1.268
- **PDBBindv2020 3D Affinity**: best Spearman correlation (0.717) among all baselines

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "QizhiPei/BioMatrix-1.7B-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

# Example: Molecule captioning with SELFIES input
instruction = "I need a brief explanation of the molecule denoted in this SELFIES notation. <|mol_sfi_start|>[Te]<|mol_sfi_end|>"

messages = [
    {"role": "user", "content": instruction}
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
print(response)
```

## Modality Wrapping

When constructing prompts, biomolecular content must be wrapped with the corresponding control tokens:

| Modality | Wrapping Example |
|----------|------------------|
| Molecule SMILES | `<\|mol_smi_start\|>CC#CC#N<\|mol_smi_end\|>` |
| Molecule SELFIES | `<\|mol_sfi_start\|>[C][#C][C][#N]<\|mol_sfi_end\|>` |
| Molecule 3D | `<\|mol_3d_start\|>[H 3][C 0][#C 6]...<\|mol_3d_end\|>` |
| Protein 1D | `<\|prot_aa_start\|><A M><A R><A A>...<\|prot_aa_end\|>` |
| Protein 3D | `<\|prot_3d_start\|><S 4012><S 153><S 2091>...<\|prot_3d_end\|>` |

Natural language text is left unwrapped and serves as the default carrier modality.

## Supported Tasks

BioMatrix-1.7B-SFT was instruction-tuned across the following task categories:

**Molecule (1D)**: unconditional generation, name conversion, property prediction, captioning, text-based generation, forward/retrosynthesis, editing, optimization, customized generation, question answering

**Molecule (3D)**: unconditional generation, property-conditioned generation

**Protein (1D)**: sequence understanding, annotation prediction, knowledge mining, text-based design, unconditional generation

**Protein (3D)**: structure understanding, folding, inverse folding, sequence-structure co-generation, unconditional backbone generation

**Interaction**: molecule-protein binding affinity (1D & 3D), protein-protein interaction

<p align="center">
  <img width="95%" src="figures/molecule_tasks_v1.png" alt="BioMatrix Molecule Tasks">
</p>

<p align="center">
  <img width="95%" src="figures/protein_tasks_v1.png" alt="BioMatrix Protein Tasks">
</p>

> **Note on task-group variants**: As detailed in the paper, the released SFT model is trained on the union of all sub-task corpora with mild oversampling for small-data tasks. For best performance on specific benchmarks, please refer to the paper's task-group-specific variants.

## SMILES vs. SELFIES

BioMatrix supports both notations as parallel 1D molecular representations. Empirically:

- **SELFIES** excels on tasks requiring validity-by-construction (unconditional generation, property optimization)
- **SMILES** excels on tasks requiring surface-level structural anchoring (customized generation with atom/bond/functional-group constraints, forward synthesis, retrosynthesis)

See Section 9.2 of the paper for detailed analysis.

## Limitations

- Molecular and protein 3D structures are tokenized in **disjoint geometric reference frames**, so the model cannot natively represent biomolecular complexes (e.g., docking poses).
- Heavy domain specialization may erode some general-purpose language capabilities of the underlying Qwen3 backbone.
- Coverage is limited to **small molecules and proteins**; nucleic acids, carbohydrates, and lipids are not currently supported.
- Fine-grained 3D geometry (e.g., bond lengths) shows residual quantization error from finite codebooks; a lightweight post-hoc force-field refinement (e.g., MMFF) closes most of this gap.

## Citation

If you find BioMatrix useful, please cite:

```bibtex
@article{pei2026biomatrix,
  title={BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language},
  author={Pei, Qizhi and Zhou, Zhimeng and Duan, Yi and Zhao, Yiyang and Li, Wei and Guo, Han and He, Liang and Li, Chengping and Hsieh, Chang-Yu and He, Conghui and Yan, Rui and Wu, Lijun},
  journal={arXiv preprint arXiv:2606.22138},
  year={2026}
}
```

## License

This model is released under the Apache 2.0 license. The base model (Qwen3-1.7B-Base) is subject to its own license terms.