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🤖 Agent-Driven Protein Engineering Platform One platform, three interfaces, infinite possibilities

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🎯 What is VenusFactory2?

VenusFactory2 is an Agent-driven protein engineering platform combining 40+ AI models with 11 biological databases. Designed for everyone — from biologists to AI researchers.

🚀 Why VenusFactory2?

🤖 Agent-First 🎯 Three Interfaces ⚡ Zero to Results
Natural language → Multi-step automation Web UI / REST API / CLI Upload → Predict in seconds
40+ models + 11 databases Same power, different styles Or train custom models in minutes

📖 Easy to Learn: Designed for life science professionals with no programming background required. Intuitive Web UI, comprehensive bilingual documentation, rich examples and video tutorials help you quickly grow from beginner to protein AI expert.

💡 Capabilities at a Glance

Task Solution Time
🧬 Mutation effects ESM-2, ProSST, ProtSSN (zero-shot) <1 min
🎯 Protein function 30+ fine-tuned models <30 sec
🔬 Custom training 7 PEFT methods (LoRA, QLoRA, etc.) 10-60 min
💾 Data download AlphaFold, UniProt, RCSB, KEGG, etc. Real-time
📚 Literature AI-powered search & analysis <2 min

⚡ Quick Start

1. Install

git clone https://github.com/AI4Protein/VenusFactory2.git && cd VenusFactory2
conda create -n venus python=3.12 && conda activate venus
pip install -r requirements.txt  # Detailed guide below ↓

2. Launch

# Web UI (Recommended)
python src/webui.py  # → http://localhost:7860

# REST API
python src/api_server.py  # → http://localhost:5000/docs

# CLI
bash script/train/train_plm_lora.sh

3. Get Results

🤖 Try Agent-0.1 | ⚡ Quick Tools | 🔬 Train Models (Click to expand examples)

Agent-0.1 (Natural Language)

Q: "Predict stability for sequence MKTAYIAKQRQISFV..."
→ Agent auto-selects model → Runs prediction → Returns results + explanations

Quick Mutation Scoring

Upload: PDB/FASTA → Mutations: A23V, K45R → Get: Stability scores

Train Your Model

Model: ESM2-650M → Dataset: DeepSol → Method: LoRA → 15 min → Trained model ✓

🤖 Agent-0.1: The Brain

Agent-0.1 orchestrates all tools via natural language. Powered by LangGraph + LangChain.

You: "Design thermostable mutations for PDB:1ABC"
         ↓
    🤖 Agent Planning
         ↓
  📥 Download → 🧬 Predict → 🎯 Score → 📊 Report
  RCSB PDB     ESM-2 scan    Stability   Ranked list
✨ Agent Capabilities
Category Features
🔬 Analysis Mutation prediction • Function/stability scoring • Structure analysis
💾 Data Multi-database search • Format conversion • Batch processing
🧠 Planning Multi-step automation • Tool orchestration • Error handling
📚 Research Literature mining • Family analysis • Report generation
💬 Example Conversations

Mutation Design:

You: "Improve thermostability of MKTAYIAKQR..."
Agent: ✓ ESM-2 scanning... ✓ Stability scoring...
→ Top 3: A5V (+2.8 kcal/mol), K9R (+1.9), T2S (+1.5)

Database Search:

You: "Find lysozyme structures <2.0Å resolution"
Agent: ✓ Searching RCSB... → Found 47 structures
→ Downloaded to: temp_outputs/lysozyme_structures/

💡 Note: Requires API key (OpenAI/Anthropic). Currently in Beta.

🏗️ Architecture

🌐 Interfaces: Web UI | REST API | CLI
        ↓
   🤖 Agent Layer (LangGraph + LangChain)
        ↓
   🔧 Application: Train | Eval | Predict | Tools
        ↓
   🛠️ Core Tools: 9 categories (mutation, database, search, etc.)
        ↓
   📊 Resources: 40+ Models | 30+ Datasets | 11+ Databases
📚 Integrated Resources

Models (40+): ESM, ProtBert, ProtT5, Venus/PETA/ProSST series

Databases (11+): AlphaFold • RCSB PDB • UniProt • NCBI • KEGG • STRING • BRENDA • ChEMBL • HPA • FDA • Foldseek

Datasets (30+): Function • Localization • Stability • Solubility • Mutation fitness

🔧 Tool Categories
Tool Description Agent CLI
🧬 Mutation ESM-1v, ESM-2, ProSST, ProtSSN, MIF-ST ✅ ✅
🎯 Prediction 30+ fine-tuned models ✅ ✅
💾 Database 11 integrations ✅ ✅
🔍 Search PubMed, FDA, patents ✅ ✅
🏋️ Training LoRA, QLoRA, DoRA, etc. ✅ ✅
📁 File Format conversion ✅ ✅
🔬 Denovo Protein design ✅ ✅
🧪 Discovery Novel discovery ✅ ✅
📊 Visualize 3D viewer ✅ ✅

🧬 Supported Models

40+ Protein Language Models (Click to expand)

Venus Series (Liang's Lab): ProSST-20/128/512/1024/2048/4096 (110M) • ProPrime-690M • VenusPLM-300M • PETA-base/bpe/unigram (80M)

ESM Series (Meta AI): ESM2: 8M, 35M, 150M, 650M, 3B, 15B • ESM-1v: 5 models (650M each)

ProtBert & ProtT5: ProtBert-Uniref100/BFD (420M) • IgBert (420M) • ProtT5-XL/XXL (3B-11B) • Ankh-base/large (450M-1.2B)

Selection Guide:

  • GPU <8GB: ESM2-8M/35M, ProSST
  • GPU 8-16GB: ESM2-150M/650M, ProtBert
  • GPU 24GB+: ESM2-3B, ProtT5-XL
  • Multi-GPU: ESM2-15B, ProtT5-XXL

By Task:

  • Classification: ESM2, ProtBert
  • Structure: Ankh
  • Generation: ProtT5
  • Antibody: IgBert/IgT5
  • Lightweight: ProSST, PETA

📚 Supported Datasets

30+ Supervised + Zero-Shot Datasets

Zero-Shot: VenusMutHub • ProteinGym (217 DMS)

Function: EC • GO_BP • GO_CC • GO_MF Localization: DeepLocBinary • DeepLocMulti • DeepLoc2Multi Stability: Thermostability • TAPE_Stability Solubility: DeepSol • DeepSoluE • eSOL • ProtSolM • PETA_CHS/LGK/TEM_Sol Mutation: FLIP_AAV (7 splits) • FLIP_GB1 (5 splits) • TAPE_Fluorescence Others: DeepET_Topt • MetalIonBinding • SortingSignal • PaCRISPR

All datasets available on HuggingFace

📦 Installation

🍎 macOS (M1/M2/M3) 
git clone https://github.com/AI4Protein/VenusFactory2.git && cd VenusFactory2
conda create -n venus python=3.12 && conda activate venus
pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
pip install torch_scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-2.8.0+cpu.html
pip install -r requirements_for_macOS.txt
🪟 Windows / 🐧 Linux (CUDA 12.8) 
git clone https://github.com/AI4Protein/VenusFactory2.git && cd VenusFactory2
conda create -n venus python=3.12 && conda activate venus
pip install torch==2.8.0 torchvision --index-url https://download.pytorch.org/whl/cu128
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.8.0+cu128.html
pip install -r requirements.txt
🪟 Windows / 🐧 Linux (CUDA 11.8) 
git clone https://github.com/AI4Protein/VenusFactory2.git && cd VenusFactory2
conda create -n venus python=3.12 && conda activate venus
pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cu118
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.7.0+cu118.html
pip install -r requirements.txt
💻 CPU Only 
git clone https://github.com/AI4Protein/VenusFactory2.git && cd VenusFactory2
conda create -n venus python=3.12 && conda activate venus
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.8.0+cpu.html
pip install -r requirements.txt

Verify: python -c "import torch; print(torch.__version__)"

🚀 Usage

Web UI

python src/webui.py  # → http://localhost:7860
Tab Purpose Features
Training Train custom models Model selection • PEFT methods • Real-time monitoring • Wandb
Evaluation Benchmark testing Load model • Select metrics • CSV export
Prediction Inference Single/batch prediction • Result visualization
Agent Natural language Multi-step automation • Tool orchestration
Quick Tools Rapid prediction Mutation scoring • Function prediction
Advanced Deep analysis Sequence/structure-based models
Download Data retrieval AlphaFold • UniProt • RCSB • InterPro
Manual Documentation Guides & tutorials
Screenshots

Training Evaluation Prediction

CLI & API

Command Line Examples 
# Train model
bash script/train/train_plm_lora.sh \
  --model facebook/esm2_t33_650M_UR50D \
  --dataset DeepSol --batch_size 32

# Evaluate
bash script/eval/eval.sh \
  --model_path ckpt/DeepSol/best_model \
  --test_dataset DeepSol

# Download data
bash script/tools/database/alphafold/download_alphafold_structure.sh
bash script/tools/database/uniprot/download_uniprot_seq.sh

# Generate structure sequences
bash script/get_structure_seq/get_esm3_structure_seq.sh
REST API Examples 
# Start server
python src/api_server.py  # → http://localhost:5000/docs

# Mutation prediction
curl -X POST http://localhost:5000/api/mutation/predict \
  -H "Content-Type: application/json" \
  -d '{"sequence": "MKTAYIA...", "mutations": ["A23V", "K45R"]}'

# Function prediction
curl -X POST http://localhost:5000/api/predict/function \
  -H "Content-Type: application/json" \
  -d '{"sequence": "MKTAYIA...", "tasks": ["solubility", "stability"]}'

# Database search
curl http://localhost:5000/api/database/uniprot/search?query=lysozyme&limit=10
Python API 
from src.tools.mutation import predict_mutation_effects
from src.tools.predict import predict_protein_function
from src.tools.database import download_alphafold_structure

# Mutations
results = predict_mutation_effects(
    sequence="MKTAYIAKQR...",
    mutations=["A5V", "K9R"],
    model="esm2"
)

# Function
predictions = predict_protein_function(
    sequence="MKTAYIA...",
    tasks=["solubility", "stability"]
)

# Data
pdb_file = download_alphafold_structure("P12345")

📊 Training Methods

Method Memory Speed Performance Best For
LoRA Low Fast Good General tasks
QLoRA Very Low Slow Good Limited GPU
DoRA Low Medium Better Improved LoRA
AdaLoRA Low Medium Better Adaptive rank
SES-Adapter Medium Medium Better Selective tuning
IA3 Very Low Fast Good Lightweight
Freeze Low Fast Good Simple tuning

🙌 Citation

@inproceedings{tan2025venusfactory,
  title={VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning},
  author={Tan, Yang and Liu, Chen and Gao, Jingyuan and Banghao, Wu and Li, Mingchen and Wang, Ruilin and Zhang, Lingrong and Yu, Huiqun and Fan, Guisheng and Hong, Liang and others},
  booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  pages={230--241},
  year={2025}
}

🎊 Acknowledgement

Developed by Liang's Lab at Shanghai Jiao Tong University.

Resources: Docs • YouTube • Playground • Issues

Made with ❤️ for the protein engineering community

⭐ Star • 🐛 Report Bug • 💡 Request Feature

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🏭 AI agent platform with skills for protein engineering, the noob-friendly AI tutorial tool for life science professionals.

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