PDFBench：基于功能的全新蛋白质设计基准测试
PDFBench: A Benchmark for De novo Protein Design from Function
AI总结：
针对功能引导的蛋白质设计缺乏统一评估框架问题，引入PDFBench基准测试，通过16个指标系统评估8个模型，涵盖两种关键设置，能可靠比较模型并为研究提供指导。
摘要：
功能引导的蛋白质设计在药物发现和酶工程中有重要应用，但缺乏统一综合评估框架。当前模型评估指标不一致且有限。我们引入PDFBench，首个功能引导的全新蛋白质设计综合基准测试，在两个关键设置下用16个指标系统评估8个模型，能更可靠比较模型并指导未来研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
