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SENET-AOP: A Computational Framework Model for Prioritizing Antioxidant Protein Targets in Drug Discovery
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DOI：10.1016/j.ejmech.2026.118945.png)
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Abstract
En 中文
• A new dataset was built with 1,144 antioxidant and 2,959 non-antioxidant proteins. • Features from two protein language models were integrated for the complementarity. • The SENet enhances the representability and interpretability of the model. • SENET-AOP achieves superior performance compared with other state-of-the-art models. • Web server and codes enable practical target identification and prioritization.
Keywords:
Antioxidant protein
Protein language model
SENet
Drug discovery
Computational framework
Journal
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5.9
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1.7W
Citations:
6.0W
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