MVPR-PF: A Multi-View Peptide Representation and Probability Fusion Framework for Anticancer Peptide Prediction

Anticancer peptides (ACPs) are promising therapeutic candidates because of their selective activity against cancer cells and relatively low toxicity toward normal cells. However, experimental ACP identification is time-consuming and costly, motivating efficient computational screening methods. This study proposes Multi-View Peptide Representation and Probability Fusion (MVPR-PF), a hybrid machine and deep learning framework that integrates Binary Profile Features (BPF), Composition of $k$-Spaced Side-Chain Pairs (CKSSCP), a 20-dimensional pI-derived physicochemical property vector, and grouped 3-mer descriptors. XGBoost learns nonlinear relationships from the structured multi-view features, while a Bidirectional Long Short-Term Memory (BiLSTM) network captures bidirectional residue dependencies from the position-aware BPF representation. Their prediction probabilities are combined through weighted probability-level soft voting. Using stratified 10-fold cross-validation, MVPR-PF achieved 86.89% accuracy and 93.66% AUC on ACP740, and 85.83% accuracy and 91.89% AUC on ACP240. These headline numbers are consistent across all reported tables, the Discussion, and the Conclusion. These results demonstrate that combining complementary handcrafted and sequential representations provides effective computational ACP prediction.