What question did this study set out to answer?

The central aim is to improve the identification and prediction of antimicrobial peptides for cancer treatment using advanced AI techniques.

March 21, 2026Scientific Reports0 citationsOpen Access

A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network

FAFarman AliBahria University MKMajdi KhalidUmm al-Qura University RARaed AlsiniKing Abdulaziz University

Key Points

The central aim is to improve the identification and prediction of antimicrobial peptides for cancer treatment using advanced AI techniques.
Developed GAC-BiTCNN-AMP, a hybrid deep learning framework combining various AI models.
Integrated a Generative Adversarial Network and Capsule Networks to enhance data diversity and model dependencies.
Utilized Bidirectional Temporal Convolutional Neural Network for sequential data analysis.
Implemented XGBoost Forward Feature Selection for identifying key sequence patterns.
Achieved 97.42% accuracy and 0.923 MCC in cross-validation tests.
Obtained 95.32% accuracy with 0.914 MCC on an independent test set.
SHAP analysis revealed significant contributions of latent representations to peptide activity.

Abstract

Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accelerated discovery of targeted therapeutics. Antimicrobial peptides (AMPs) have emerged as promising candidates for cancer treatment due to their selective cytotoxicity, immunomodulatory properties, and ability to alter the tumor microenvironment. However, their accurate computational identification remains challenging because existing models struggle to capture the complex structural and functional determinants of AMP activity. In this study, we propose GAC-BiTCNN-AMP, a hybrid generative and explainable deep learning framework designed to advance peptide discovery for precision oncology. The architecture integrates a Generative Adversarial Network to enhance data diversity, Capsule Networks to model hierarchical molecular dependencies, and a Bidirectional Temporal Convolutional Neural Network for capturing contextual sequence information. To strengthen biological signal representation, the model incorporates embeddings from advanced protein language model including ProtTrans-T5, UniRep, and ESM-2 alongside a novel PsePSSM-DCT evolutionary descriptor. A wrapper-based XGBoost Forward Feature Selection strategy further refines the feature space by identifying the most discriminative sequence patterns. GAC-BiTCNN-AMP delivers strong predictive performance, achieving 97.42% accuracy and 0.923 MCC in cross-validation, and 95.32% accuracy with 0.914 MCC on the same independent test set. SHapley Additive exPlanations (SHAP) analysis highlights key contributions from the fused latent representations to peptide activity, demonstrating the framework's interpretability at the representation level. By integrating generative modeling, deep representation learning, and explainable AI, this study provides a scalable computational pipeline supporting therapeutic peptide discovery for targeted, immune-modulatory, and precision cancer applications.

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