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

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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Publication Details

Journal
Current Drug Targets
Published
2026-01-20
DOI
https://doi.org/10.2174/0113894501416864251212082231
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan, Hafsah Riasat
Current Drug Targets
Machine Learning in Bioinformatics
article

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

Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan, Hafsah Riasat
article en

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.

Current Drug TargetsVol. 27
Qassim University (SA), University of Management and Technology (PK), Buraydah Colleges (SA)
Openalex Percentile: Top 77%
Machine Learning in Bioinformatics
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