March 3, 2026

XCPP: A Multi-model Explainable Deep Learning Framework forAccurate Identification of Cell-Penetrating Peptides from StructuredSequence Features

Key Points

Accurate identification of cell-penetrating peptides enhances molecular diagnostics and drug delivery applications.
The framework utilizes a CNN model, offering advanced identification capabilities and improved trust through explainability.
Deep learning methods, particularly SHAP-based XAI, enhance the interpretability of peptide identification predictions.
Potential applications include personalized medicine, indicating significant advancements in tailored therapeutic approaches.

Abstract

Deep learning in this case has created a framework that can accurately identify CPPs. The CNN model also has additional features that make it ideal for use in drug delivery, molecular diagnostics, and personalized medicine. SHAP-based XAI further adds explainability, increasing trust in the model's predictions.

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Authors

Hafsah Riasat

Tamim Alkhalifah

Fahad Alturise

Journals

Current Drug Targets

Actions

Institutions

Qassim University

Buraydah Colleges

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XCPP: A Multi-model Explainable Deep Learning Framework forAccurate Identification of Cell-Penetrating Peptides from StructuredSequence Features

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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