ProteinBench: A Holistic Evaluation of Protein Foundation Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916425091776512 |
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| author | Ye, Fei Zheng, Zaixiang Xue, Dongyu Shen, Yuning Wang, Lihao Ma, Yiming Wang, Yan Wang, Xinyou Zhou, Xiangxin Gu, Quanquan |
| author_facet | Ye, Fei Zheng, Zaixiang Xue, Dongyu Shen, Yuning Wang, Lihao Ma, Yiming Wang, Yan Wang, Xinyou Zhou, Xiangxin Gu, Quanquan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06744 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProteinBench: A Holistic Evaluation of Protein Foundation Models Ye, Fei Zheng, Zaixiang Xue, Dongyu Shen, Yuning Wang, Lihao Ma, Yiming Wang, Yan Wang, Xinyou Zhou, Xiangxin Gu, Quanquan Quantitative Methods Artificial Intelligence Machine Learning Biomolecules 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. |
| title | ProteinBench: A Holistic Evaluation of Protein Foundation Models |
| topic | Quantitative Methods Artificial Intelligence Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2409.06744 |