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Model: QizhiPei/BioMatrix-4B-SFT Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- biology
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- chemistry
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- molecule
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- protein
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- multimodal
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- foundation-model
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- drug-discovery
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- protein-design
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pipeline_tag: text-generation
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base_model: QizhiPei/BioMatrix-4B-Base
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library_name: transformers
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---
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# BioMatrix-4B-SFT
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**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.
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This is the **4B-parameter SFT (Supervised Fine-Tuned)** variant, instruction-tuned across 80 downstream biological tasks spanning 6 categories.
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- 📄 **Paper**: [BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language](https://github.com/QizhiPei/BioMatrix/blob/main/biomatrix_tech_report.pdf)
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- 💻 **Code**: [https://github.com/QizhiPei/BioMatrix](https://github.com/QizhiPei/BioMatrix)
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- 🤗 **Model & Data Collection**: [https://huggingface.co/collections/QizhiPei/biomatrix](https://huggingface.co/collections/QizhiPei/biomatrix)
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## Model Overview
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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:
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- **Molecular 1D sequences** (both SMILES and SELFIES notations)
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- **Molecular 3D structures** (via MolStrucTok with branch-decoupled decoder)
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- **Protein 1D sequences** (residue-level tokens)
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- **Protein 3D structures** (via GCP-VQVAE backbone tokenizer)
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- **Natural language** (inherited from Qwen3 tokenizer)
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All modalities are consumed and produced uniformly under a **single next-token prediction objective**—without external encoders, projection adapters, or modality-specific output heads.
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| Model | Molecule 1D | Molecule 3D | Protein 1D | Protein 3D | Natural Language |
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|-------|:-----------:|:-----------:|:----------:|:----------:|:----------------:|
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| ESM3 | ✗ | ✗ | ✓ | ✓ | ✓ |
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| 3D-MoLM | ✓ | ✓ | ✗ | ✗ | ✓ |
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| AlphaFold3 | ✓ | ✓ | ✓ | ✓ | ✗ |
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| BioT5/BioT5+ | ✓ | ✗ | ✓ | ✗ | ✓ |
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| BioMedGPT | ✓ | ✗ | ✓ | ✗ | ✓ |
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| NatureLM | ✓ | ✗ | ✓ | ✗ | ✓ |
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| SciReasoner | ✓ | ✗ | ✓ | ✗ | ✓ |
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| **BioMatrix** | **✓** | **✓** | **✓** | **✓** | **✓** |
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## Model Details
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- **Base Architecture**: Qwen3-4B-Base
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- **Parameters**: 4B
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- **Training Stages**:
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- **Continual Pretraining** on 304.4B tokens (general/scientific text, molecular & protein 1D/3D data, cross-modal interleaved corpora)
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- **Instruction Tuning** on a comprehensive suite of 80 downstream tasks across 6 categories
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- **Context Length**: 8,192 tokens
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- **Tokenizer**: Extended Qwen3 vocabulary with:
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- 11,294 joint molecular 3D tokens (composed from SELFIES atom × MolStrucTok codes)
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- 4,096 protein 3D tokens (GCP-VQVAE codebook)
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- 26 protein 1D tokens (amino acids + non-standard/unknown)
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- SELFIES atom tokens and modality-specific control tokens
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## Pretraining Corpus (304.4B tokens)
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| Category | Tokens | Sources |
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|----------|--------|---------|
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| **Text** | 105.3B | FineWeb-Edu, FineFineWeb (biology/chemistry/medical/health), PubMed Full Articles |
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| **Molecule** | 73.7B | PubChem, PCQM4Mv2, PubChemQC, MolTextNet |
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| **Protein** | 77.4B | UniRef50, RCSB PDB, Swiss-Prot, TrEMBL, AlphaFold DB |
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| **Cross-entity** | 48.0B | Interleaved text (PubMed, bioRxiv, S2ORC, USPTO), Molecule–protein (BindingDB, STITCH, jglaser, CrossDocked), Protein–protein (AlphaSeq, PPIRef) |
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## Performance Highlights
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BioMatrix achieves **state-of-the-art or competitive performance on 77 out of 80 tasks**. Selected highlights for the 4B-SFT variant:
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### Molecular Tasks
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- **Unconditional 1D Generation** (GuacaMol): 0.998 validity, 1.000 uniqueness, 0.986 novelty
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- **Name Conversion (I2S EM)**: 92.83% (vs. SciReasoner-8B: 84.40%)
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- **Text-Based Molecule Generation (EM)**: 65.07% (vs. SciReasoner-8B: 48.00%)
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- **MoleculeQA Total Accuracy**: 73.78% (vs. prior best MolCA-1.3B: 64.79%)
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- **Property-Conditioned 3D Generation**: ~3-4× error reduction on QM9 electronic-structure targets
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### Protein Tasks
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- **Fold Type Prediction (Family level)**: 87.25% accuracy
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- **Annotation Prediction (UniProtSeq Keywords F1)**: 91.26%
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- **Inverse Folding AAR**: 75.50% (vs. DPLM-2-3B: 61.67%)
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- **Sequence–Structure Co-generation**: scTM = 0.965, scRMSD = 2.80
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- **Unconditional Backbone Generation**: scTM = 0.963 (joint frontier with RFDiffusion)
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### Interaction Tasks
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- **BindingDB Affinity (RMSE)**: 1.030 (new SOTA, surpasses prior literature SOTA of 1.340)
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- **PDBBindv2020 3D Affinity**: RMSE = 1.260, Pearson = 0.737, MAE = 0.972
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "QizhiPei/BioMatrix-4B-SFT"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True
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)
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# Example: Molecule captioning with SELFIES input
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instruction = "I need a brief explanation of the molecule denoted in this SELFIES notation. <|mol_sfi_start|>[Te]<|mol_sfi_end|>"
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messages = [
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{"role": "user", "content": instruction}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
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print(response)
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```
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## Modality Wrapping
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When constructing prompts, biomolecular content must be wrapped with the corresponding control tokens:
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| Modality | Wrapping Example |
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|----------|------------------|
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| Molecule SMILES | `<\|mol_smi_start\|>CC#CC#N<\|mol_smi_end\|>` |
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| Molecule SELFIES | `<\|mol_sfi_start\|>[C][#C][C][#N]<\|mol_sfi_end\|>` |
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| Molecule 3D | `<\|mol_3d_start\|>[H 3][C 0][#C 6]...<\|mol_3d_end\|>` |
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| Protein 1D | `<\|prot_aa_start\|><A M><A R><A A>...<\|prot_aa_end\|>` |
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| Protein 3D | `<\|prot_3d_start\|><S 4012><S 153><S 2091>...<\|prot_3d_end\|>` |
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Natural language text is left unwrapped and serves as the default carrier modality.
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## Supported Tasks
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BioMatrix-4B-SFT was instruction-tuned across the following task categories:
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**Molecule (1D)**: unconditional generation, name conversion, property prediction, captioning, text-based generation, forward/retrosynthesis, editing, optimization, customized generation, question answering
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**Molecule (3D)**: unconditional generation, property-conditioned generation
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**Protein (1D)**: sequence understanding, annotation prediction, knowledge mining, text-based design, unconditional generation
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**Protein (3D)**: structure understanding, folding, inverse folding, sequence-structure co-generation, unconditional backbone generation
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**Interaction**: molecule-protein binding affinity (1D & 3D), protein-protein interaction
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> **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.
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## SMILES vs. SELFIES
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BioMatrix supports both notations as parallel 1D molecular representations. Empirically:
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- **SELFIES** excels on tasks requiring validity-by-construction (unconditional generation, property optimization)
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- **SMILES** excels on tasks requiring surface-level structural anchoring (customized generation with atom/bond/functional-group constraints, forward synthesis, retrosynthesis)
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See Section 9.2 of the paper for detailed analysis.
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## Limitations
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- Molecular and protein 3D structures are tokenized in **disjoint geometric reference frames**, so the model cannot natively represent biomolecular complexes (e.g., docking poses).
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- Heavy domain specialization may erode some general-purpose language capabilities of the underlying Qwen3 backbone.
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- Coverage is limited to **small molecules and proteins**; nucleic acids, carbohydrates, and lipids are not currently supported.
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- 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.
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## Citation
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If you find BioMatrix useful, please cite:
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```bibtex
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@article{pei2026biomatrix,
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title={BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language},
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author={Pei, Qizhi and Zhou, Zhimeng and Duan, Yi and Zhao, Yiyang and He, Liang and Hsieh, Chang-Yu and He, Conghui and Yan, Rui and Wu, Lijun},
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year={2026}
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}
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```
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## License
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This model is released under the Apache 2.0 license. The base model (Qwen3-4B-Base) is subject to its own license terms.
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