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General Protein Pretraining or Domain-Specific Designs? Benchmarking Protein Modeling on Realistic Applications
arXiv – CS AI|Shuo Yan, Yuliang Yan, Bin Ma, Chenao Li, Haochun Tang, Jiahua Lu, Minhua Lin, Yuyuan Feng, Enyan Dai||3 views
🤖AI Summary
Researchers introduce Protap, a comprehensive benchmark comparing protein modeling approaches across realistic applications. The study finds that large-scale pretrained models often underperform supervised encoders on small datasets, while structural information and domain-specific biological knowledge can enhance specialized protein tasks.
Key Takeaways
- →Large-scale pretraining encoders often underperform supervised encoders when trained on small downstream datasets.
- →Incorporating structural information during fine-tuning can match or outperform protein language models pretrained on large sequence corpora.
- →Domain-specific biological priors enhance performance on specialized downstream tasks like enzyme cleavage prediction.
- →Protap benchmark includes industrially relevant tasks missing from existing benchmarks, such as targeted protein degradation.
- →The research provides open-source code and datasets for reproducible protein modeling comparisons.
#protein-modeling#machine-learning#benchmarking#pretraining#bioinformatics#deep-learning#protein-structure#research#open-source
Read Original →via arXiv – CS AI
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