Cancer-ACPNet: A Two-Stage ESM-2 and Capsule Network Framework for Anticancer Peptide Screening and Cancer-Type Activity Prediction.

Authors

Adeel Ashraf, Dong-Jun Yu

Published in

IEEE journal of biomedical and health informatics. Volume PP. Sep 11, 2026. Epub Sep 11, 2026.

Abstract

Anticancer peptides (ACPs) are being widely studied as potential peptide-based anticancer agents because of their ability to interact selectively with cancer cells. Despite this promise, computational identification of ACPs and prediction of their cancer-type activity remain difficult, mainly because available peptide datasets are small, imbalanced, and biologically heterogeneous. In this study, we propose a two-stage framework for ACP identification and functional cancer-type annotation. In Stage 1, ESM-2 sequence embeddings are combined with BLOSUM62 and AAIndex-based descriptors and processed through a capsule-enhanced classifier to distinguish ACPs from non-ACPs. In Stage 2, the learned ACP representation is reused for one-vs-all prediction across seven cancer types using an ensemble of MLP heads with meta-stacking, calibration, class-aware thresholds, and MC-dropout inference. The Cancer-ACPNet Stage-1 model achieved accuracies of 84.08%, 96.74%, and 84.75% on Set 1, Set 2, and Set 3, respectively, with balanced F1-score and MCC values. For Stage 2, the model achieved strong seven cancer-type activity prediction, with macro-average accuracy, F1-score, MCC, and AUC values of 89.37%, 88.18%, 77.79%, and 92.77%, respectively. Model explanation analyses based on saliency, SHAP, residue enrichment, mutation validation, meta-stacker coefficients, and end-to-end residue perturbation identified residue regions and physicochemical patterns that influenced the predictions. These results are interpreted as model-level explanations requiring further experimental validation. In general, this framework provides an efficient means of screening potential ACPs and assigning probable cancer-type activities, which helps to prioritize candidate peptides to be experimentally tested.

PMID:
42726617
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

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