XCPP: A Multi-model Explainable Deep Learning Framework for Accurate Identification of Cell-Penetrating Peptides from Structured Sequence Features.
Quick Facts
What This Study Found
Key Numbers
How They Did This
Why This Research Matters
What This Study Doesn't Tell Us
Trust & Context
- Original Title:
- XCPP: A Multi-model Explainable Deep Learning Framework for Accurate Identification of Cell-Penetrating Peptides from Structured Sequence Features.
- Published In:
- Current drug targets (2026)
- Database ID:
- RPEP-15995
Evidence Hierarchy
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Cite This Study
https://rethinkpeptides.com/research/RPEP-15995APA
Riasat, Hafsah; Alkhalifah, Tamim; Alturise, Fahad; Khan, Yaser Daanial. (2026). XCPP: A Multi-model Explainable Deep Learning Framework for Accurate Identification of Cell-Penetrating Peptides from Structured Sequence Features.. Current drug targets. https://doi.org/10.2174/0113894501416864251212082231
MLA
Riasat, Hafsah, et al. "XCPP: A Multi-model Explainable Deep Learning Framework for Accurate Identification of Cell-Penetrating Peptides from Structured Sequence Features.." Current drug targets, 2026. https://doi.org/10.2174/0113894501416864251212082231
RethinkPeptides
RethinkPeptides Research Database. "XCPP: A Multi-model Explainable Deep Learning Framework for ..." RPEP-15995. Retrieved from https://rethinkpeptides.com/research/riasat-2026-xcpp-a-multimodel-explainable
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Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.