March 3, 2026

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

Puntos clave

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.

Resumen

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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Cite this study

Riasat et al. (Tue,) studied this question.

synapsesocial.com/papers/69a75aaec6e9836116a20cff — DOI: https://doi.org/10.2174/0113894501416864251212082231

Authors

Hafsah Riasat

Tamim Alkhalifah

Fahad Alturise

Journals

Current Drug Targets

Actions

Institutions

Qassim University

Buraydah Colleges

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

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

Puntos clave

Resumen

Citation Network

Connected Papers

Discussion

Cite this study

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion