CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction

Yehong Han and Lin Du

PLOS ONE, 2026, vol. 21, issue 2, 1-19

Abstract: Accurate prediction of potential drug-drug interactions (DDIs) is vital for ensuring medication safety and efficacy. Existing graph-based methods typically focus on molecular structures but often overlook the complementary semantic information embedded in SMILES (Simplified Molecular Input Line Entry System) representations. To address this gap, we propose CMCL-DDI, a Cross-view Mutual Contrastive Learning framework that jointly leverages pharmacophore-aware molecular graphs and SMILES sequences. Specifically, we encode pharmacophore-based subgraphs to capture functional molecular features and aggregate them into expressive graph-level embeddings. In parallel, SMILES sequences are encoded to preserve sequential drug characteristics. A contrastive learning strategy aligns both views in a shared latent space, facilitating mutual representation enhancement. Furthermore, we design a cross-attention fusion module to integrate heterogeneous features, enabling robust and interpretable DDI prediction. Extensive experiments on benchmark datasets demonstrate that CMCL-DDI consistently outperforms state-of-the-art models, highlighting the effectiveness of cross-view representation learning for DDI prediction. The source codes are available at https://github.com/95LY/CMCL-DDI.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0341952 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 41952&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0341952

DOI: 10.1371/journal.pone.0341952

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().