SENET-AOP: A Computational Framework Model for Prioritizing Antioxidant Protein Targets in Drug Discovery

Shaobin Chen

doi:10.1016/j.ejmech.2026.118945

SENET-AOP: A Computational Framework Model for Prioritizing Antioxidant Protein Targets in Drug Discovery

2026-05-09

0

PRE

AI

S

Shaobin Chen

Y

Yuhan Zhu

J

Jinrun Liu

Y

Yezi Liu

S

Sisi He

X

Xiaolei Zhu *

DOI：10.1016/j.ejmech.2026.118945

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Abstract

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• 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

SENET-AOP: A Computational Framework Model for Prioritizing Antioxidant Protein Targets in Drug Discovery

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