@inproceedings{tan-etal-2025-venusfactory,
    title = "{V}enus{F}actory: 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
      Zhou, Bingxin",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.23/",
    pages = "230--241",
    ISBN = "979-8-89176-253-4",
    abstract = "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/ai4protein/VenusFactory. A video introduction is available at https://www.youtube.com/watch?v=MT6lPH5kgCc."
}