PDFBench: A Benchmark for De novo Protein Design from Function

• URL: http://arxiv.org/abs/2505.20346v2
• Date: Sun, 28 Sep 2025 03:52:13 GMT
• Title: PDFBench: A Benchmark for De novo Protein Design from Function
• Authors: Jiahao Kuang, Nuowei Liu, Jie Wang, Changzhi Sun, Tao Ji, Yuanbin Wu,
• Abstract summary: PDFBench is the first comprehensive benchmark for function-guided denovo protein design.<n>Our benchmark systematically evaluates eight state-of-the-art models on 16 metrics across two key settings.
• Score: 18.373430158468874
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering. However, the field lacks a unified and comprehensive evaluation framework. Current models are assessed using inconsistent and limited subsets of metrics, which prevents fair comparison and a clear understanding of the relationships between different evaluation criteria. To address this gap, we introduce PDFBench, the first comprehensive benchmark for function-guided denovo protein design. Our benchmark systematically evaluates eight state-of-the-art models on 16 metrics across two key settings: description-guided design, for which we repurpose the Mol-Instructions dataset, originally lacking quantitative benchmarking, and keyword-guided design, for which we introduce a new test set, SwissTest, created with a strict datetime cutoff to ensure data integrity. By benchmarking across a wide array of metrics and analyzing their correlations, PDFBench enables more reliable model comparisons and provides key insights to guide future research.

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