Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention

Yundian Zeng1,2, Qing Ye1,2, Jike Wang1,3

  • 1Zhejiang University, Hangzhou, Zhejiang 310058, China.

Journal of Chemical Information and Modeling
|July 13, 2026
PubMed
Summary

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.

Acta pharmaceutica Sinica. B·2026
Same author

AI decodes protein-ligand binding.

Nature chemical biology·2026
Same author

Clinical efficacy and safety of subtotal resection of adenomyotic lesions based on the Kishi classification: a retrospective case series study.

Frontiers in medicine·2026
Same author

Weighted single-step GWAS identified candidate genes associated with semen traits in Rhode Island Red chickens.

Poultry science·2026
Same author

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Nature protocols·2026
Same author

Development and internal validation of a clinical prediction model for postoperative urinary tract infection in older surgical patients: a retrospective cohort study.

BMC geriatrics·2026

ProphDR, an interpretable AI framework, accurately predicts cancer drug responses using multiomics and drug structures. It offers biological insights for precision oncology and drug repurposing.

Area of Science:

  • Computational biology
  • Artificial intelligence in oncology
  • Genomics and drug discovery

Background:

  • Accurate prediction of cancer drug responses (CDRs) is crucial but challenging due to tumor complexity.
  • Existing machine learning models often lack interpretability, hindering clinical translation.

Purpose of the Study:

  • To develop an interpretable deep learning framework, ProphDR, for accurate and explainable prediction of CDRs.
  • To integrate multiomics data and drug structural information for enhanced predictive power.

Main Methods:

  • ProphDR utilizes a hierarchical attention mechanism to integrate multiomics data and drug structures.
  • Key modules include Criss-Cross Gene-level Multiomics Integration (CGMI) and cross-attention (CA) for drug-gene interaction modeling.

Related Experiment Videos

Main Results:

  • ProphDR achieved state-of-the-art performance in predicting ln(IC50) values (PCC=0.938, RMSE=0.978) and drug sensitivity (AUC=0.981) on GDSC and CCLE datasets.
  • The framework demonstrated strong generalizability in cold-start scenarios (unseen drugs/cell lines).
  • Interpretable attention maps identified key pharmacophores and resistance genes (e.g., ERBB2), aligning with known cancer mechanisms.

Conclusions:

  • ProphDR provides a robust and explainable AI tool for advancing precision oncology.
  • The framework bridges genomic features with phenotypic drug responses, aiding target prioritization and drug repurposing.
  • Interpretability of ProphDR facilitates biological understanding and clinical application in cancer therapy.