PDFBench: A Benchmark for De Novo Protein Design from Function

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 PDF-BENCH, the first comprehensive benchmark for function-guided de novo protein design. Our benchmark systematically evaluates eight stateof-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 keywordguided 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. The codes and datasets are available in https://github.com/PDFBench/ PDFBench .