Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units

Zhengyun Zhao, Jingyu Gui, Anqi Yao, Nguyen Quoc Khanh Le, Matthew Chin Heng Chua

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

In recent times, the importance of peptides in the biomedical domain has received increasing concern in terms of their effect on multiple disease treatments. However, before successful large-scale implementation in the industry, accurate identification of peptide toxicity is a vital prerequisite. The existing computational methods have reached good results from toxicity prediction, and we present an improved model based on different deep learning architectures. The modification mainly focuses on two aspects: sequence encoding and variational information bottlenecks. Consequently, one of our modified plans shows an obvious increase in sensitivity, while the rest show good performance meanwhile adding novelty in the peptide toxicity prediction domain. In detail, our best model could achieve an accuracy of 97.38 and 95.03% in protein and peptide toxicity predictions, respectively. The performance was superior to previous predictors on the same datasets.

Original languageEnglish
Pages (from-to)40569-40577
Number of pages9
JournalACS Omega
Volume7
Issue number44
DOIs
Publication statusPublished - Nov 8 2022

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering

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