ProteinBench: A Holistic Evaluation of Protein Foundation Models
Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu
2024-09-12
Summary
This paper talks about ProteinBench, a new evaluation framework designed to assess the performance of protein foundation models used in predicting and designing proteins.
What's the problem?
As more protein foundation models are developed, it becomes difficult to understand their strengths and weaknesses because there isn't a standard way to evaluate them. Without a unified evaluation framework, researchers can't easily compare models or know how well they perform in different tasks related to proteins.
What's the solution?
To address this issue, the authors created ProteinBench, which includes three main parts: a classification system for different protein-related tasks, a multi-metric approach to evaluate models based on quality, novelty, diversity, and robustness, and detailed analyses from various user perspectives. This comprehensive evaluation helps clarify how well these models work and where they might fall short.
Why it matters?
This research is important because it provides a structured way to evaluate protein foundation models, which can lead to better understanding and improvements in protein research. By making the evaluation process transparent and accessible, ProteinBench can help drive advancements in fields like drug discovery and biotechnology.
Abstract
Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce ProteinBench, a holistic evaluation framework designed to enhance the transparency of protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance. Our comprehensive evaluation of protein foundation models reveals several key findings that shed light on their current capabilities and limitations. To promote transparency and facilitate further research, we release the evaluation dataset, code, and a public leaderboard publicly for further analysis and a general modular toolkit. We intend for ProteinBench to be a living benchmark for establishing a standardized, in-depth evaluation framework for protein foundation models, driving their development and application while fostering collaboration within the field.