arXiv:2505.20346v3 Announce Type: replace-cross 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
The rapid advancements in AI for scientific discovery, especially in protein folding and generation, necessitate better evaluation frameworks to ensure progress and comparison.
A unified benchmark like PDFBench will accelerate the development of function-guided protein design, crucial for drug discovery and enzyme engineering, by providing consistent evaluation and fostering innovation.
The ability to reliably compare and improve de novo protein design models will be enhanced, leading to more targeted and effective biological engineering applications.
- · Biopharmaceutical companies
- · Synthetic biology researchers
- · AI model developers
- · Drug discovery sector
- · Companies relying on outdated protein design methodologies
- · Fragmented research efforts
- · Untested AI models
PDFBench standardizes evaluation for function-guided protein design, making model comparisons more robust.
Improved protein design accelerates the discovery of novel therapeutics and industrial enzymes, impacting healthcare and manufacturing.
Enhanced synthetic biology capabilities could lead to programmable biological systems for entirely new industries or solutions to global challenges.
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Read at arXiv cs.AI