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	---
	title: VenusFactory
	app_file: app.py
	sdk: gradio
	sdk_version: 5.24.0
	---
	<div align="right">
	<a href="README.md">English</a> \| <a href="README_CN.md">简体中文</a>
	</div>

	<p align="center">
	<img src="img/banner_2503.png" width="70%" alt="VenusFactory Banner">
	</p>

	<div align="center">

	[![GitHub stars](https://img.shields.io/github/stars/tyang816/VenusFactory?style=flat-square)](https://github.com/tyang816/VenusFactory/stargazers) [![GitHub forks](https://img.shields.io/github/forks/tyang816/VenusFactory?style=flat-square)](https://github.com/tyang816/VenusFactory/network/members) [![GitHub issues](https://img.shields.io/github/issues/tyang816/VenusFactory?style=flat-square)](https://github.com/tyang816/VenusFactory/issues) [![GitHub license](https://img.shields.io/github/license/tyang816/VenusFactory?style=flat-square)](https://github.com/tyang816/VenusFactory/blob/main/LICENSE)
	[![Python Version](https://img.shields.io/badge/Python-3.10-blue?style=flat-square&logo=python)](https://www.python.org/) [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen?style=flat-square)](https://venusfactory.readthedocs.io/) [![Downloads](https://img.shields.io/github/downloads/tyang816/VenusFactory/total?style=flat-square)](https://github.com/tyang816/VenusFactory/releases)

	</div>

	Recent News:

	- Welcome to VenusFactory! This project is developed by [Liang's Lab](https://lianglab.sjtu.edu.cn/) at [Shanghai Jiao Tong University](https://www.sjtu.edu.cn/).
	- [2025-03-26] Add [VenusPLM-300M](https://huggingface.co/AI4Protein/VenusPLM-300M) model, trained based on VenusPod, is a protein language model independently developed by Hong Liang's research group at Shanghai Jiao Tong University.
	- [2025-03-17] Add [Venus-PETA, Venus-ProPrime, Venus-ProSST models](https://huggingface.co/AI4Protein), for more details, please refer to [Supported Models](#-supported-models)
	- [2025-03-05] 🎉 Congratulations! 🎉

	🚀 Our latest research achievement, VenusMutHub, has been officially accepted by [Acta Pharmaceutica Sinica B](https://www.sciencedirect.com/science/article/pii/S2211383525001650) and is now featured in a series of [leaderboards](https://lianglab.sjtu.edu.cn/muthub/)!
	💡 In this study, we built 900+ high-quality benchmark [datasets](https://huggingface.co/datasets/AI4Protein/VenusMutHub) covering 500+ protein functional properties. VenusMutHub not only offers a new collection of small-sample datasets for real-world protein mutation engineering, but also fills the gap in diversity within existing benchmarks, laying a stronger foundation for AI-driven protein mutation effect prediction.


	## ✏️ Table of Contents

	- [Features](#-features)
	- [Supported Models](#-supported-models)
	- [Supported Training Approaches](#-supported-training-approaches)
	- [Supported Datasets](#-supported-datasets)
	- [Supported Metrics](#-supported-metrics)
	- [Requirements](#-requirements)
	- [Installation Guide](#-installation-guide)
	- [Quick Start with Venus Web UI](#-quick-start-with-venus-web-ui)
	- [Code-line Usage](#-code-line-usage)
	- [Citation](#-citation)
	- [Acknowledgement](#-acknowledgement)

	## 📑 Features

	- Vaious protein langugae models: Venus series, ESM series, ProtTrans series, Ankh series, etc
	- Comprehensive supervised datasets: Localization, Fitness, Solubility, Stability, etc
	- Easy and quick data collector: AlphaFold2 Database, RCSB, InterPro, Uniprot, etc
	- Experiment moitors: Wandb, Local
	- Friendly interface: Gradio UI

	## 🤖 Supported Models

	### Pre-training Protein Language Models

	<details>
	<summary>Venus Series Models (Published by Liang's Lab)</summary>

	\| Model \| Size \| Parameters \| GPU Memory \| Features \| Template \|
	\|-------\|------\|------------\|------------\|----------\|----------\|
	\| ProSST-20 \| 20 \| 110M \| 4GB+ \| Mutation \| [AI4Protein/ProSST-20](https://huggingface.co/AI4Protein/ProSST-20) \|
	\| ProSST-128 \| 128 \| 110M \| 4GB+ \| Mutation \| [AI4Protein/ProSST-128](https://huggingface.co/AI4Protein/ProSST-128) \|
	\| ProSST-512 \| 512 \| 110M \| 4GB+ \| Mutation \| [AI4Protein/ProSST-512](https://huggingface.co/AI4Protein/ProSST-512) \|
	\| ProSST-2048 \| 2048 \| 110M \| 4GB+ \| Mutation \| [AI4Protein/ProSST-2048](https://huggingface.co/AI4Protein/ProSST-2048) \|
	\| ProSST-4096 \| 4096 \| 110M \| 4GB+ \| Mutation \| [AI4Protein/ProSST-4096](https://huggingface.co/AI4Protein/ProSST-4096) \|
	\| ProPrime-690M \| 690M \| 690M \| 16GB+ \| OGT-prediction \| [AI4Protein/Prime_690M](https://huggingface.co/AI4Protein/Prime_690M) \|
	\| VenusPLM-300M \| 300M \| 300M \| 12GB+ \| Protein-language \| [AI4Protein/VenusPLM-300M](https://huggingface.co/AI4Protein/VenusPLM-300M) \|

	> 💡 These models often excel in specific tasks or offer unique architectural benefits
	</details>

	<details>
	<summary>Venus-PETA Models: Tokenization variants</summary>

	#### BPE Tokenization Series
	\| Model \| Vocab Size \| Parameters \| GPU Memory \| Template \|
	\|-------\|------------\|------------\|------------\|----------\|
	\| PETA-base \| base \| 80M \| 4GB+ \| [AI4Protein/deep_base](https://huggingface.co/AI4Protein/deep_base) \|
	\| PETA-bpe-50 \| 50 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_50](https://huggingface.co/AI4Protein/deep_bpe_50) \|
	\| PETA-bpe-200 \| 200 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_200](https://huggingface.co/AI4Protein/deep_bpe_200) \|
	\| PETA-bpe-400 \| 400 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_400](https://huggingface.co/AI4Protein/deep_bpe_400) \|
	\| PETA-bpe-800 \| 800 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_800](https://huggingface.co/AI4Protein/deep_bpe_800) \|
	\| PETA-bpe-1600 \| 1600 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_1600](https://huggingface.co/AI4Protein/deep_bpe_1600) \|
	\| PETA-bpe-3200 \| 3200 \| 80M \| 4GB+ \| [AI4Protein/deep_bpe_3200](https://huggingface.co/AI4Protein/deep_bpe_3200) \|

	#### Unigram Tokenization Series
	\| Model \| Vocab Size \| Parameters \| GPU Memory \| Template \|
	\|-------\|------------\|------------\|------------\|----------\|
	\| PETA-unigram-50 \| 50 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_50](https://huggingface.co/AI4Protein/deep_unigram_50) \|
	\| PETA-unigram-100 \| 100 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_100](https://huggingface.co/AI4Protein/deep_unigram_100) \|
	\| PETA-unigram-200 \| 200 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_200](https://huggingface.co/AI4Protein/deep_unigram_200) \|
	\| PETA-unigram-400 \| 400 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_400](https://huggingface.co/AI4Protein/deep_unigram_400) \|
	\| PETA-unigram-800 \| 800 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_800](https://huggingface.co/AI4Protein/deep_unigram_800) \|
	\| PETA-unigram-1600 \| 1600 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_1600](https://huggingface.co/AI4Protein/deep_unigram_1600) \|
	\| PETA-unigram-3200 \| 3200 \| 80M \| 4GB+ \| [AI4Protein/deep_unigram_3200](https://huggingface.co/AI4Protein/deep_unigram_3200) \|

	> 💡 Different tokenization strategies may be better suited for specific tasks
	</details>

	<details>
	<summary>ESM Series Models: Meta AI's protein language models</summary>

	\| Model \| Size \| Parameters \| GPU Memory \| Training Data \| Template \|
	\|-------\|------\|------------\|------------\|---------------\|----------\|
	\| ESM2-8M \| 8M \| 8M \| 2GB+ \| UR50/D \| [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) \|
	\| ESM2-35M \| 35M \| 35M \| 4GB+ \| UR50/D \| [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) \|
	\| ESM2-150M \| 150M \| 150M \| 8GB+ \| UR50/D \| [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) \|
	\| ESM2-650M \| 650M \| 650M \| 16GB+ \| UR50/D \| [facebook/esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) \|
	\| ESM2-3B \| 3B \| 3B \| 24GB+ \| UR50/D \| [facebook/esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) \|
	\| ESM2-15B \| 15B \| 15B \| 40GB+ \| UR50/D \| [facebook/esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) \|
	\| ESM-1b \| 650M \| 650M \| 16GB+ \| UR50/S \| [facebook/esm1b_t33_650M_UR50S](https://huggingface.co/facebook/esm1b_t33_650M_UR50S) \|
	\| ESM-1v-1 \| 650M \| 650M \| 16GB+ \| UR90/S \| [facebook/esm1v_t33_650M_UR90S_1](https://huggingface.co/facebook/esm1v_t33_650M_UR90S_1) \|
	\| ESM-1v-2 \| 650M \| 650M \| 16GB+ \| UR90/S \| [facebook/esm1v_t33_650M_UR90S_2](https://huggingface.co/facebook/esm1v_t33_650M_UR90S_2) \|
	\| ESM-1v-3 \| 650M \| 650M \| 16GB+ \| UR90/S \| [facebook/esm1v_t33_650M_UR90S_3](https://huggingface.co/facebook/esm1v_t33_650M_UR90S_3) \|
	\| ESM-1v-4 \| 650M \| 650M \| 16GB+ \| UR90/S \| [facebook/esm1v_t33_650M_UR90S_4](https://huggingface.co/facebook/esm1v_t33_650M_UR90S_4) \|
	\| ESM-1v-5 \| 650M \| 650M \| 16GB+ \| UR90/S \| [facebook/esm1v_t33_650M_UR90S_5](https://huggingface.co/facebook/esm1v_t33_650M_UR90S_5) \|

	> 💡 ESM2 models are the latest generation, offering better performance than ESM-1b/1v
	</details>

	<details>
	<summary>BERT-based Models: Transformer encoder architecture</summary>

	\| Model \| Size \| Parameters \| GPU Memory \| Training Data \| Template \|
	\|-------\|------\|------------\|------------\|---------------\|----------\|
	\| ProtBert-Uniref100 \| 420M \| 420M \| 12GB+ \| UniRef100 \| [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) \|
	\| ProtBert-BFD \| 420M \| 420M \| 12GB+ \| BFD100 \| [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) \|
	\| IgBert \| 420M \| 420M \| 12GB+ \| Antibody \| [Exscientia/IgBert](https://huggingface.co/Exscientia/IgBert) \|
	\| IgBert-unpaired \| 420M \| 420M \| 12GB+ \| Antibody \| [Exscientia/IgBert_unpaired](https://huggingface.co/Exscientia/IgBert_unpaired) \|

	> 💡 BFD-trained models generally show better performance on structure-related tasks
	</details>

	<details>
	<summary>T5-based Models: Encoder-decoder architecture</summary>

	\| Model \| Size \| Parameters \| GPU Memory \| Training Data \| Template \|
	\|-------\|------\|------------\|------------\|---------------\|----------\|
	\| ProtT5-XL-UniRef50 \| 3B \| 3B \| 24GB+ \| UniRef50 \| [Rostlab/prot_t5_xl_uniref50](https://huggingface.co/Rostlab/prot_t5_xl_uniref50) \|
	\| ProtT5-XXL-UniRef50 \| 11B \| 11B \| 40GB+ \| UniRef50 \| [Rostlab/prot_t5_xxl_uniref50](https://huggingface.co/Rostlab/prot_t5_xxl_uniref50) \|
	\| ProtT5-XL-BFD \| 3B \| 3B \| 24GB+ \| BFD100 \| [Rostlab/prot_t5_xl_bfd](https://huggingface.co/Rostlab/prot_t5_xl_bfd) \|
	\| ProtT5-XXL-BFD \| 11B \| 11B \| 40GB+ \| BFD100 \| [Rostlab/prot_t5_xxl_bfd](https://huggingface.co/Rostlab/prot_t5_xxl_bfd) \|
	\| IgT5 \| 3B \| 3B \| 24GB+ \| Antibody \| [Exscientia/IgT5](https://huggingface.co/Exscientia/IgT5) \|
	\| IgT5-unpaired \| 3B \| 3B \| 24GB+ \| Antibody \| [Exscientia/IgT5_unpaired](https://huggingface.co/Exscientia/IgT5_unpaired) \|
	\| Ankh-base \| 450M \| 450M \| 12GB+ \| Encoder-decoder \| [ElnaggarLab/ankh-base](https://huggingface.co/ElnaggarLab/ankh-base) \|
	\| Ankh-large \| 1.2B \| 1.2B \| 20GB+ \| Encoder-decoder \| [ElnaggarLab/ankh-large](https://huggingface.co/ElnaggarLab/ankh-large) \|

	> 💡 T5 models can be used for both encoding and generation tasks
	</details>

	### Model Selection Guide

	<details>
	<summary>How to choose the right model?</summary>

	1. Based on Hardware Constraints:
	- Limited GPU (<8GB): ESM2-8M, ESM2-35M, ProSST
	- Medium GPU (8-16GB): ESM2-150M, ESM2-650M, ProtBert series
	- High-end GPU (24GB+): ESM2-3B, ProtT5-XL, Ankh-large
	- Multiple GPUs: ESM2-15B, ProtT5-XXL

	2. Based on Task Type:
	- Sequence classification: ESM2, ProtBert
	- Structure prediction: ESM2, Ankh
	- Generation tasks: ProtT5
	- Antibody design: IgBert, IgT5
	- Lightweight deployment: ProSST, PETA-base

	3. Based on Training Data:
	- General protein tasks: ESM2, ProtBert
	- Structure-aware tasks: Ankh
	- Antibody-specific: IgBert, IgT5
	- Custom tokenization needs: PETA series

	</details>

	> 🔍 All models are available through the Hugging Face Hub and can be easily loaded using their templates.

	## 🔬 Supported Training Approaches

	<details>
	<summary>Supported Training Approaches</summary>

	\| Approach \| Full-tuning \| Freeze-tuning \| SES-Adapter \| AdaLoRA \| QLoRA \| LoRA \| DoRA \| IA3 \|
	\| ---------------------- \| ----------- \| ------------------ \| ------------------ \| ------------------ \|----------- \| ------------------ \| -----------------\| -----------------\|
	\| Supervised Fine-Tuning \| ✅ \| ✅ \| ✅ \| ✅ \|✅ \| ✅ \| ✅ \| ✅ \|

	</details>

	## 📚 Supported Datasets

	<details><summary>Pre-training datasets</summary>

	\| dataset \| data level \| link \|
	\|------------\|------\|------\|
	\| CATH_V43_S40 \| structures \| [CATH_V43_S40](https://huggingface.co/datasets/tyang816/cath) \|
	\| AGO_family \| structures \| [AGO_family](https://huggingface.co/datasets/tyang816/Ago_database_PDB) \|

	</details>

	<details><summary>Zero-shot datasets</summary>

	\| dataset \| task \| link \|
	\|------------\|------\|------\|
	\| VenusMutHub \| mutation effects prediction \| [VenusMutHub](https://huggingface.co/datasets/AI4Protein/VenusMutHub) \|
	\| ProteinGym \| mutation effects prediction \| [ProteinGym](https://proteingym.org/) \|

	</details>

	<details><summary>Supervised fine-tuning datasets (amino acid sequences/ foldseek sequences/ ss8 sequences)</summary>

	\| dataset \| task \| data level \| problem type \| link \|
	\|------------\|------\|----------\|----------\|------\|
	\| DeepLocBinary \| localization \| protein-wise \| single_label_classification \| [DeepLocBinary_AlphaFold2](https://huggingface.co/datasets/tyang816/DeepLocBinary_AlphaFold2), [DeepLocBinary_ESMFold](https://huggingface.co/datasets/tyang816/DeepLocBinary_ESMFold) \|
	\| DeepLocMulti \| localization \| protein-wise \| multi_label_classification \| [DeepLocMulti_AlphaFold2](https://huggingface.co/datasets/tyang816/DeepLocMulti_AlphaFold2), [DeepLocMulti_ESMFold](https://huggingface.co/datasets/tyang816/DeepLocMulti_ESMFold) \|
	\| DeepLoc2Multi \| localization \| protein-wise \| single_label_classification \| [DeepLoc2Multi_AlphaFold2](https://huggingface.co/datasets/tyang816/DeepLoc2Multi_AlphaFold2), [DeepLoc2Multi_ESMFold](https://huggingface.co/datasets/tyang816/DeepLoc2Multi_ESMFold) \|
	\| DeepSol \| solubility \| protein-wise \| single_label_classification \| [DeepSol_ESMFold](https://huggingface.co/datasets/tyang816/DeepSol_ESMFold) \|
	\| DeepSoluE \| solubility \| protein-wise \| single_label_classification \| [DeepSoluE_ESMFold](https://huggingface.co/datasets/tyang816/DeepSoluE_ESMFold) \|
	\| ProtSolM \| solubility \| protein-wise \| single_label_classification \| [ProtSolM_ESMFold](https://huggingface.co/datasets/tyang816/ProtSolM_ESMFold) \|
	\| eSOL \| solubility \| protein-wise \| regression \| [eSOL_AlphaFold2](https://huggingface.co/datasets/tyang816/eSOL_AlphaFold2), [eSOL_ESMFold](https://huggingface.co/datasets/tyang816/eSOL_ESMFold) \|
	\| DeepET_Topt \| optimum temperature \| protein-wise \| regression \| [DeepET_Topt_AlphaFold2](https://huggingface.co/datasets/tyang816/DeepET_Topt_AlphaFold2), [DeepET_Topt_ESMFold](https://huggingface.co/datasets/tyang816/DeepET_Topt_ESMFold) \|
	\| EC \| function \| protein-wise \| multi_label_classification \| [EC_AlphaFold2](https://huggingface.co/datasets/tyang816/EC_AlphaFold2), [EC_ESMFold](https://huggingface.co/datasets/tyang816/EC_ESMFold) \|
	\| GO_BP \| function \| protein-wise \| multi_label_classification \| [GO_BP_AlphaFold2](https://huggingface.co/datasets/tyang816/GO_BP_AlphaFold2), [GO_BP_ESMFold](https://huggingface.co/datasets/tyang816/GO_BP_ESMFold) \|
	\| GO_CC \| function \| protein-wise \| multi_label_classification \| [GO_CC_AlphaFold2](https://huggingface.co/datasets/tyang816/GO_CC_AlphaFold2), [GO_CC_ESMFold](https://huggingface.co/datasets/tyang816/GO_CC_ESMFold) \|
	\| GO_MF \| function \| protein-wise \| multi_label_classification \| [GO_MF_AlphaFold2](https://huggingface.co/datasets/tyang816/GO_MF_AlphaFold2), [GO_MF_ESMFold](https://huggingface.co/datasets/tyang816/GO_MF_ESMFold) \|
	\| MetalIonBinding \| binding \| protein-wise \| single_label_classification \| [MetalIonBinding_AlphaFold2](https://huggingface.co/datasets/tyang816/MetalIonBinding_AlphaFold2), [MetalIonBinding_ESMFold](https://huggingface.co/datasets/tyang816/MetalIonBinding_ESMFold) \|
	\| Thermostability \| stability \| protein-wise \| regression \| [Thermostability_AlphaFold2](https://huggingface.co/datasets/tyang816/Thermostability_AlphaFold2), [Thermostability_ESMFold](https://huggingface.co/datasets/tyang816/Thermostability_ESMFold) \|

	> ✨ Only structural sequences are different for the same dataset, for example, ``DeepLocBinary_ESMFold`` and ``DeepLocBinary_AlphaFold2`` share the same amino acid sequences, this means if you only want to use the ``aa_seqs``, both are ok!

	</details>

	<details><summary>Supervised fine-tuning datasets (amino acid sequences)</summary>

	\| dataset \| task \| data level \| problem type \| link \|
	\|------------\|------\|----------\|----------\|------\|
	\| Demo_Solubility \| solubility \| protein-wise \| single_label_classification \| [Demo_Solubility](https://huggingface.co/datasets/tyang816/Demo_Solubility) \|
	\| DeepLocBinary \| localization \| protein-wise \| single_label_classification \| [DeepLocBinary](https://huggingface.co/datasets/tyang816/DeepLocBinary) \|
	\| DeepLocMulti \| localization \| protein-wise \| multi_label_classification \| [DeepLocMulti](https://huggingface.co/datasets/tyang816/DeepLocMulti) \|
	\| DeepLoc2Multi \| localization \| protein-wise \| single_label_classification \| [DeepLoc2Multi](https://huggingface.co/datasets/tyang816/DeepLoc2Multi) \|
	\| DeepSol \| solubility \| protein-wise \| single_label_classification \| [DeepSol](https://huggingface.co/datasets/tyang816/DeepSol) \|
	\| DeepSoluE \| solubility \| protein-wise \| single_label_classification \| [DeepSoluE](https://huggingface.co/datasets/tyang816/DeepSoluE) \|
	\| ProtSolM \| solubility \| protein-wise \| single_label_classification \| [ProtSolM](https://huggingface.co/datasets/tyang816/ProtSolM) \|
	\| eSOL \| solubility \| protein-wise \| regression \| [eSOL](https://huggingface.co/datasets/tyang816/eSOL) \|
	\| DeepET_Topt \| optimum temperature \| protein-wise \| regression \| [DeepET_Topt](https://huggingface.co/datasets/tyang816/DeepET_Topt) \|
	\| EC \| function \| protein-wise \| multi_label_classification \| [EC](https://huggingface.co/datasets/tyang816/EC) \|
	\| GO_BP \| function \| protein-wise \| multi_label_classification \| [GO_BP](https://huggingface.co/datasets/tyang816/GO_BP) \|
	\| GO_CC \| function \| protein-wise \| multi_label_classification \| [GO_CC](https://huggingface.co/datasets/tyang816/GO_CC) \|
	\| GO_MF \| function \| protein-wise \| multi_label_classification \| [GO_MF](https://huggingface.co/datasets/tyang816/GO_MF) \|
	\| MetalIonBinding \| binding \| protein-wise \| single_label_classification \| [MetalIonBinding](https://huggingface.co/datasets/tyang816/MetalIonBinding) \|
	\| Thermostability \| stability \| protein-wise \| regression \| [Thermostability](https://huggingface.co/datasets/tyang816/Thermostability) \|
	\| PaCRISPR \| CRISPR \| protein-wise \| single_label_classification \| [PaCRISPR](https://huggingface.co/datasets/tyang816/PaCRISPR) \|
	\| PETA_CHS_Sol \| solubility \| protein-wise \| single_label_classification \| [PETA_CHS_Sol](https://huggingface.co/datasets/tyang816/PETA_CHS_Sol) \|
	\| PETA_LGK_Sol \| solubility \| protein-wise \| single_label_classification \| [PETA_LGK_Sol](https://huggingface.co/datasets/tyang816/PETA_LGK_Sol) \|
	\| PETA_TEM_Sol \| solubility \| protein-wise \| single_label_classification \| [PETA_TEM_Sol](https://huggingface.co/datasets/tyang816/PETA_TEM_Sol) \|
	\| SortingSignal \| sorting signal \| protein-wise \| single_label_classification \| [SortingSignal](https://huggingface.co/datasets/tyang816/SortingSignal) \|
	\| FLIP_AAV \| mutation \| protein-site \| regression \|
	\| FLIP_AAV_one-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_one-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_AAV_one-vs-rest) \|
	\| FLIP_AAV_two-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_two-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_AAV_two-vs-rest) \|
	\| FLIP_AAV_mut-des \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_mut-des](https://huggingface.co/datasets/tyang816/FLIP_AAV_mut-des) \|
	\| FLIP_AAV_des-mut \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_des-mut](https://huggingface.co/datasets/tyang816/FLIP_AAV_des-mut) \|
	\| FLIP_AAV_seven-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_seven-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_AAV_seven-vs-rest) \|
	\| FLIP_AAV_low-vs-high \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_low-vs-high](https://huggingface.co/datasets/tyang816/FLIP_AAV_low-vs-high) \|
	\| FLIP_AAV_sampled \| mutation \| protein-site \| single_label_classification \| [FLIP_AAV_sampled](https://huggingface.co/datasets/tyang816/FLIP_AAV_sampled) \|
	\| FLIP_GB1 \| mutation \| protein-site \| regression \|
	\| FLIP_GB1_one-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_GB1_one-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_GB1_one-vs-rest) \|
	\| FLIP_GB1_two-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_GB1_two-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_GB1_two-vs-rest) \|
	\| FLIP_GB1_three-vs-rest \| mutation \| protein-site \| single_label_classification \| [FLIP_GB1_three-vs-rest](https://huggingface.co/datasets/tyang816/FLIP_GB1_three-vs-rest) \|
	\| FLIP_GB1_low-vs-high \| mutation \| protein-site \| single_label_classification \| [FLIP_GB1_low-vs-high](https://huggingface.co/datasets/tyang816/FLIP_GB1_low-vs-high) \|
	\| FLIP_GB1_sampled \| mutation \| protein-site \| single_label_classification \| [FLIP_GB1_sampled](https://huggingface.co/datasets/tyang816/FLIP_GB1_sampled) \|
	\| TAPE_Fluorescence \| fluorescence \| protein-site \| regression \| [TAPE_Fluorescence](https://huggingface.co/datasets/tyang816/TAPE_Fluorescence) \|
	\| TAPE_Stability \| stability \| protein-site \| regression \| [TAPE_Stability](https://huggingface.co/datasets/tyang816/TAPE_Stability) \|

	</details>

	## 📈 Supported Metrics

	<details>
	<summary>Supported Metrics</summary>

	\| Name \| Torchmetrics \| Problem Type \|
	\| ------------- \| ---------------- \| ------------------------------------------------------- \|
	\| accuracy \| Accuracy \| single_label_classification/ multi_label_classification \|
	\| recall \| Recall \| single_label_classification/ multi_label_classification \|
	\| precision \| Precision \| single_label_classification/ multi_label_classification \|
	\| f1 \| F1Score \| single_label_classification/ multi_label_classification \|
	\| mcc \| MatthewsCorrCoef \| single_label_classification/ multi_label_classification \|
	\| auc \| AUROC \| single_label_classification/ multi_label_classification \|
	\| f1_max \| F1ScoreMax \| multi_label_classification \|
	\| spearman_corr \| SpearmanCorrCoef \| regression \|
	\| mse \| MeanSquaredError \| regression \|

	</details>

	## ✈️ Requirements

	### Hardware Requirements
	- Recommended: NVIDIA RTX 3090 (24GB) or better
	- Actual requirements depend on your chosen protein language model

	### Software Requirements
	- [Anaconda3](https://www.anaconda.com/download) or [Miniconda3](https://docs.conda.io/projects/miniconda/en/latest/)
	- Python 3.10

	## 📦 Installation Guide
	<details><summary> Git start with macOS</summary>

	## To achieve the best performance and experience, we recommend using Mac devices with M-series chips (such as M1, M2, M3, etc.).

	## 1️⃣ Clone the repository

	First, get the VenusFactory code:

	```bash
	git clone https://github.com/tyang816/VenusFactory.git
	cd VenusFactory
	```

	## 2️⃣ Create a Conda environment

	Ensure you have Anaconda or Miniconda installed. Then, create a new environment named `venus` with Python 3.10:

	```bash
	conda create -n venus python=3.10
	conda activate venus
	```

	## 3️⃣ Install Pytorch and PyG dependencies

	```bash
	# Install PyTorch
	pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

	# Install PyG dependencies
	pip install torch_scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-2.2.0+cpu.html
	```

	## 4️⃣ Install remaining dependencies

	Install the remaining dependencies using `requirements_for_macOS.txt`:
	```bash
	pip install -r requirements_for_macOS.txt
	```
	</details>

	<details><summary> Git start with Windows or Linux on CUDA 12.x</summary>

	## We recommend using CUDA 12.2


	## 1️⃣ Clone the repository

	First, get the VenusFactory code:

	```bash
	git clone https://github.com/tyang816/VenusFactory.git
	cd VenusFactory
	```

	## 2️⃣ Create a Conda environment

	Ensure you have Anaconda or Miniconda installed. Then, create a new environment named `venus` with Python 3.10:

	```bash
	conda create -n venus python=3.10
	conda activate venus
	```

	## 3️⃣ Install Pytorch and PyG dependencies

	```bash
	# Install PyTorch
	pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121

	# Install PyG dependencies
	pip install torch_geometric==2.6.1 -f https://pytorch-geometric.com/whl/torch-2.5.1+cu121.html
	pip install --no-index torch_scatter==2.1.2 -f https://pytorch-geometric.com/whl/torch-2.5.1+cu121.html
	```

	## 4️⃣ Install remaining dependencies

	Install the remaining dependencies using `requirements.txt`:
	```bash
	pip install -r requirements.txt
	```
	</details>

	<details><summary> Git start with Windows or Linux on CUDA 11.x</summary>

	## We recommend using CUDA 11.8 or later versions, as they support higher versions of PyTorch, providing a better experience.


	## 1️⃣ Clone the repository

	First, get the VenusFactory code:

	```bash
	git clone https://github.com/tyang816/VenusFactory.git
	cd VenusFactory
	```

	## 2️⃣ Create a Conda environment

	Ensure you have Anaconda or Miniconda installed. Then, create a new environment named `venus` with Python 3.10:

	```bash
	conda create -n venus python=3.10
	conda activate venus
	```

	## 3️⃣ Install Pytorch and PyG dependencies

	```bash
	# Install PyTorch
	pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu118

	# Install PyG dependencies
	pip install torch_geometric==2.6.1 -f https://pytorch-geometric.com/whl/torch-2.5.1+cu118.html
	pip install --no-index torch_scatter==2.1.2 -f https://pytorch-geometric.com/whl/torch-2.5.1+cu118.html
	```

	## 4️⃣ Install remaining dependencies

	Install the remaining dependencies using `requirements.txt`:
	```bash
	pip install -r requirements.txt
	```
	</details>

	<details><summary> Git start with Windows or Linux on CPU</summary>

	## 1️⃣ Clone the repository

	First, get the VenusFactory code:

	```bash
	git clone https://github.com/tyang816/VenusFactory.git
	cd VenusFactory
	```

	## 2️⃣ Create a Conda environment

	Ensure you have Anaconda or Miniconda installed. Then, create a new environment named `venus` with Python 3.10:

	```bash
	conda create -n venus python=3.10
	conda activate venus
	```

	## 3️⃣ Install Pytorch and PyG dependencies

	```bash
	# Install PyTorch
	pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cpu

	# Install PyG dependencies
	pip install torch_geometric==2.6.1 -f https://pytorch-geometric.com/whl/torch-2.5.1+cpu.html
	pip install --no-index torch_scatter==2.1.2 -f https://pytorch-geometric.com/whl/torch-2.5.1+cpu.html
	```

	## 4️⃣ Install remaining dependencies

	Install the remaining dependencies using `requirements.txt`:
	```bash
	pip install -r requirements.txt
	```
	</details>

	## 🚀 Quick Start with Venus Web UI

	### Start Venus Web UI

	Get started quickly with our intuitive graphical interface powered by [Gradio](https://github.com/gradio-app/gradio):

	```bash
	python ./src/webui.py
	```

	This will launch the Venus Web UI where you can:
	- Configure and run fine-tuning experiments
	- Monitor training progress
	- Evaluate models
	- Visualize results

	### Using Each Tab

	We provide a detailed guide to help you navigate through each tab of the Venus Web UI.

	<details>
	<summary>1. Training Tab: Train your own protein language model</summary>

	![Model_Dataset_Config](img/Train/Model_Dataset_Config.png)

	Select a protein language model from the dropdown menu. Upload your dataset or select from available datasets and choose metrics appropriate for your problem type.

	![Training_Parameters](img/Train/Training_Parameters.png)
	Choose a training method (Freeze, SES-Adapter, LoRA, QLoRA etc.) and configure training parameters (batch size, learning rate, etc.).

	![Preview_Command](img/Train/Preview_Command.png)
	![Training_Progress](img/Train/Training_Progress.png)
	![Best_Model](img/Train/Best_Model.png)
	![Monitor_Figs](img/Train/Monitor_Figs.png)
	Click "Start Training" and monitor progress in real-time.

	<p align="center">
	<img src="img/Train/Metric_Results.png" width="60%" alt="Metric_Results">
	</p>

	Click "Download CSV" to download the test metrics results.
	</details>

	<details>
	<summary>2. Evaluation Tab: Evaluate your trained model within a benchmark</summary>

	![Model_Dataset_Config](img/Eval/Model_Dataset_Config.png)

	Load your trained model by specifying the model path. Select the same protein language model and model configs used during training. Select a test dataset and configure batch size. Choose evaluation metrics appropriate for your problem type. Finally, click "Start Evaluation" to view performance metrics.
	</details>

	<details>
	<summary>3. Prediction Tab: Use your trained model to predict samples</summary>

	![Predict_Tab](img/Predict/Predict_Tab.png)

	Load your trained model by specifying the model path. Select the same protein language model and model configs used during training.

	For single sequence: Enter a protein sequence in the text box.

	For batch prediction: Upload a CSV file with sequences.

	![Batch](img/Predict/Batch.png)

	Click "Predict" to generate and view results.
	</details>

	<details>
	<summary>4. Download Tab: Collect data from different sources with high efficiency</summary>

	- AlphaFold2 Structures: Enter UniProt IDs to download protein structures
	- UniProt: Search for protein information using keywords or IDs
	- InterPro: Retrieve protein family and domain information
	- RCSB PDB: Download experimental protein structures
	</details>

	<details>
	<summary>5. Manual Tab: Detailed documentation and guides</summary>

	Select a language (English/Chinese).

	Navigate through the documentation using the table of contents and find step-by-step guides.
	</details>

	## 🧬 Code-line Usage

	For users who prefer command-line interface, we provide comprehensive script solutions for different scenarios.

	<details>
	<summary>Training Methods: Various fine-tuning approaches for different needs</summary>

	### Full Model Fine-tuning
	```bash
	# Freeze-tuning: Train only specific layers while freezing others
	bash ./script/train/train_plm_vanilla.sh
	```

	### Parameter-Efficient Fine-tuning (PEFT)
	```bash
	# SES-Adapter: Selective and Efficient adapter fine-tuning
	bash ./script/train/train_plm_ses-adapter.sh

	# AdaLoRA: Adaptive Low-Rank Adaptation
	bash ./script/train/train_plm_adalora.sh

	# QLoRA: Quantized Low-Rank Adaptation
	bash ./script/train/train_plm_qlora.sh

	# LoRA: Low-Rank Adaptation
	bash ./script/train/train_plm_lora.sh

	# DoRA: Double Low-Rank Adaptation
	bash ./script/train/train_plm_dora.sh

	# IA3: Infused Adapter by Inhibiting and Amplifying Inner Activations
	bash ./script/train/train_plm_ia3.sh
	```

	#### Training Method Comparison
	\| Method \| Memory Usage \| Training Speed \| Performance \|
	\|--------\|--------------\|----------------\|-------------\|
	\| Freeze \| Low \| Fast \| Good \|
	\| SES-Adapter \| Medium \| Medium \| Better \|
	\| AdaLoRA \| Low \| Medium \| Better \|
	\| QLoRA \| Very Low \| Slower \| Good \|
	\| LoRA \| Low \| Fast \| Good \|
	\| DoRA \| Low \| Medium \| Better \|
	\| IA3 \| Very Low \| Fast \| Good \|

	</details>

	<details>
	<summary>Model Evaluation: Comprehensive evaluation tools</summary>

	### Basic Evaluation
	```bash
	# Evaluate model performance on test sets
	bash ./script/eval/eval.sh
	```

	### Available Metrics
	- Classification: accuracy, precision, recall, F1, MCC, AUC
	- Regression: MSE, Spearman correlation
	- Multi-label: F1-max

	### Visualization Tools
	- Training curves
	- Confusion matrices
	- ROC curves
	- Performance comparison plots

	</details>

	<details>
	<summary>Structure Sequence Tools: Process protein structure information</summary>

	### ESM Structure Sequence
	```bash
	# Generate structure sequences using ESM-3
	bash ./script/get_get_structure_seq/get_esm3_structure_seq.sh
	```

	### Secondary Structure
	```bash
	# Predict protein secondary structure
	bash ./script/get_get_structure_seq/get_secondary_structure_seq.sh
	```

	Features:
	- Support for multiple sequence formats
	- Batch processing capability
	- Integration with popular structure prediction tools

	</details>

	<details>
	<summary>Data Collection Tools: Multi-source protein data acquisition</summary>

	### Format Conversion
	```bash
	# Convert CIF format to PDB
	bash ./crawler/convert/maxit.sh
	```

	### Metadata Collection
	```bash
	# Download metadata from RCSB PDB
	bash ./crawler/metadata/download_rcsb.sh
	```

	### Sequence Data
	```bash
	# Download protein sequences from UniProt
	bash ./crawler/sequence/download_uniprot_seq.sh
	```

	### Structure Data
	```bash
	# Download from AlphaFold2 Database
	bash ./crawler/structure/download_alphafold.sh

	# Download from RCSB PDB
	bash ./crawler/structure/download_rcsb.sh
	```

	Features:
	- Automated batch downloading
	- Resume interrupted downloads
	- Data integrity verification
	- Multiple source support
	- Customizable search criteria

	#### Supported Databases
	\| Database \| Data Type \| Access Method \| Rate Limit \|
	\|----------\|-----------\|---------------\|------------\|
	\| AlphaFold2 \| Structures \| REST API \| Yes \|
	\| RCSB PDB \| Structures \| FTP/HTTP \| No \|
	\| UniProt \| Sequences \| REST API \| Yes \|
	\| InterPro \| Domains \| REST API \| Yes \|

	</details>

	<details>
	<summary>Usage Examples: Common scenarios and solutions</summary>

	### Training Example
	```bash
	# Train a protein solubility predictor using ESM2
	bash ./script/train/train_plm_lora.sh \
	--model "facebook/esm2_t33_650M_UR50D" \
	--dataset "DeepSol" \
	--batch_size 32 \
	--learning_rate 1e-4
	```

	### Evaluation Example
	```bash
	# Evaluate the trained model
	bash ./script/eval/eval.sh \
	--model_path "path/to/your/model" \
	--test_dataset "DeepSol_test"
	```

	### Data Collection Example
	```bash
	# Download structures for a list of UniProt IDs
	bash ./crawler/structure/download_alphafold.sh \
	--input uniprot_ids.txt \
	--output ./structures
	```

	</details>

	> 💡 All scripts support additional command-line arguments for customization. Use `--help` with any script to see available options.

	## 🙌 Citation

	Please cite our work if you have used our code or data.

	```bibtex
	@article{tan2025venusfactory,
	title={VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning},
	author={Tan, Yang and Liu, Chen and Gao, Jingyuan and Wu, Banghao and Li, Mingchen and Wang, Ruilin and Zhang, Lingrong and Yu, Huiqun and Fan, Guisheng and Hong, Liang and Zhou, Bingxin},
	journal={arXiv preprint arXiv:2503.15438},
	year={2025}
	}
	```

	## 🎊 Acknowledgement

	Thanks the support of [Liang's Lab](https://ins.sjtu.edu.cn/people/lhong/index.html).