Momentum Contrast-Enhanced Multimodal Representation Learning for Drug Synergy Prediction

Clicks: 7
ID: 321457
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #681 of 850 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 850 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Accurate prediction of synergistic drug combinations can accelerate anticancer combination discovery. Existing methods inadequately model higher-order drug-drug-cell-line interactions and drug-disease associations and remain sensitive to sparse and noisy multi-omics data, limiting generalization to unseen cell lines and drug combinations. RESULTS: We present MoCo-MultiSynergy, a multimodal framework that combines modality-specific momentum contrastive learning with heterogeneous hypergraph modeling. The hypergraph represents synergistic drug-drug-cell-line triplets and drug-disease associations, while gated residual propagation refines node representations. Momentum Contrast modules regularize encoded drug and cell-line representations using latent feature masking and Gaussian perturbation. On the O'Neil and NCI-ALMANAC datasets, MoCo-MultiSynergy achieves the highest AUROC and AUPRC across the evaluated settings, with the largest gains when generalizing to unseen cell lines and drug combinations. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/27167199/MoCo-MultiSynergy.
Reference Key
openalex_W7169533362 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu Gu, Xindi Huang, Lifen Shi, Le Chen, Guihua Duan, Cheng Yan
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag530
URL
https://doi.org/10.1093/bioinformatics/btag530
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.