ArticleBioinformatics (Oxford, England)2023
PDBench: evaluating computational methods for protein-sequence design.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- From atoms to fragments: a coarse representation for efficient and functional protein design.Bioinformatics (Oxford, England) · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.PloS one · 2025Article
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- What does it take for an 'AlphaFold Moment' in functional protein engineering and design?Nature biotechnology · 2024Article
- Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.Bioinformatics (Oxford, England) · 2024Article
- TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.Protein engineering, design & selection : PEDS · 2024Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Protein Design with Deep Learning.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
4 authors.
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Abstract
summaryEver increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target. AVAILABILITY AND IMPLEMENTATION: https://github.com/wells-wood-research/PDBench. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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