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AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM

Most Jebun Nahar Juthy1,2, S M Hasan Mahmud3,4, Md Faruk Hosen5,6

  • 1Department of ICT, Mawlana Bhashani Science and Technology University (MBSTU), Santosh, 1902, Tangail, Bangladesh.

Scientific Reports
|December 1, 2025
PubMed
Summary

This study introduces AttBiLSTM_DE, a computational framework using deep learning to accurately predict anticancer peptides (ACPs). This AI-driven approach accelerates the discovery of novel cancer treatments, improving patient outcomes.

Keywords:
Anticancer peptidesAttention mechanismBidirectional LSTMDifferential EvolutionWord Embedding

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Cancer is a leading cause of death globally, with current treatments causing significant side effects.
  • Anticancer peptides (ACPs) show promise for targeted cancer therapy, but experimental identification is costly and time-consuming.

Purpose of the Study:

  • To develop an advanced computational framework for accurate prediction of anticancer peptides (ACPs).
  • To overcome the limitations of experimental screening for novel ACPs.
  • To aid in the development of new cancer treatments and drug discovery.

Main Methods:

  • Utilized Natural Language Processing (NLP) techniques (One-Hot Encoding, GloVe, fastText, Word2Vec) and k-mer embedding for peptide sequence representation.
  • Developed a Differential Evolution (DE) algorithm for feature optimization and weight assignment.
  • Employed an Attention-based Bidirectional Long Short-Term Memory (BiLSTM) model for enhanced prediction.

Main Results:

  • The AttBiLSTM_DE model achieved a high prediction accuracy of 95.85% and an Area Under the Curve (AUC) of 98.48%.
  • Outperformed existing attention-based deep learning models in predicting ACPs.
  • Demonstrated the framework's effectiveness in identifying potential anticancer peptides.

Conclusions:

  • The AttBiLSTM_DE framework offers a powerful and efficient computational tool for ACP prediction.
  • This approach can significantly accelerate the discovery of novel anticancer peptides for therapeutic development.
  • An accessible online web server is available for real-time ACP prediction.