# VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning

arXiv: https://arxiv.org/abs/2503.15438 · HTML: https://mlsanity.com/paper/2503.15438/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2303.10512/) (2023)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models](https://mlsanity.com/paper/2306.12420/) (2023)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks](https://mlsanity.com/paper/2310.15469/) (2023)
- [Exploring evolution-aware & -free protein language models as protein function predictors](https://mlsanity.com/paper/2206.06583/) (2022)
- [Large scale paired antibody language models](https://mlsanity.com/paper/2403.17889/) (2024)
- [DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information](https://mlsanity.com/paper/2407.01585/) (2024)

## Built on by (most cited first, 3 of 3)

- [VenusX: Unlocking Fine-Grained Functional Understanding of Proteins](https://mlsanity.com/paper/2505.11812/) (2025)
- [PFMBench: Protein Foundation Model Benchmark](https://mlsanity.com/paper/2506.14796/) (2025)
- [MIMIC: A Generative Multimodal Foundation Model for Biomolecules](https://mlsanity.com/paper/2604.24506/) (2026)

## Abstract (arXiv)

Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task benchmarking, and application. This work presents VenusFactory, a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. VenusFactory supports both computer science and biology communities with choices of both a command-line execution and a Gradio-based no-code interface, integrating 40+ protein-related datasets and 40+ popular PLMs. All implementations are open-sourced on https://github.com/tyang816/VenusFactory.