# PDFBench: A Benchmark for De novo Protein Design from Function

arXiv: https://arxiv.org/abs/2505.20346 · HTML: https://mlsanity.com/paper/2505.20346/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [ProGen2: Exploring the Boundaries of Protein Language Models](https://mlsanity.com/paper/2206.13517/) (2022)
- [Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models](https://mlsanity.com/paper/2306.08018/) (2023)
- [Diffusion Language Models Are Versatile Protein Learners](https://mlsanity.com/paper/2402.18567/) (2024)
- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [A Text-guided Protein Design Framework](https://mlsanity.com/paper/2302.04611/) (2023)
- [DPLM-2: A Multimodal Diffusion Protein Language Model](https://mlsanity.com/paper/2410.13782/) (2024)
- [Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds](https://mlsanity.com/paper/2301.12485/) (2023)
- [RITA: a Study on Scaling Up Generative Protein Sequence Models](https://mlsanity.com/paper/2205.05789/) (2022)
- [De novo protein design using geometric vector field networks](https://mlsanity.com/paper/2310.11802/) (2023)
- [Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space](https://mlsanity.com/paper/2405.18986/) (2024)
- [Annotation-guided Protein Design with Multi-Level Domain Alignment](https://mlsanity.com/paper/2404.16866/) (2024)
- [EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations](https://mlsanity.com/paper/2412.11618/) (2024)
- [Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion](https://mlsanity.com/paper/2407.07443/) (2024)

## Built on by (most cited first, 5 of 5)

- [ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design](https://mlsanity.com/paper/2604.16896/) (2026)
- [Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards](https://mlsanity.com/paper/2603.26378/) (2026)
- [AMix-2: Establishing Protein as a Native Modality in Large Language Models](https://mlsanity.com/paper/2605.30963/) (2026)
- [ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design](https://mlsanity.com/paper/2605.10189/) (2026)
- [Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling](https://mlsanity.com/paper/2608.16094/) (2026)

## Abstract (arXiv)

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.