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

## TL;DR

This paper introduces a new method for predicting drug-drug interactions by combining molecular structure and SMILES data using contrastive learning.

## Contribution

The novel contribution is a cross-view contrastive learning framework that integrates pharmacophore-aware graphs and SMILES sequences for DDI prediction.

## Key findings

- CMCL-DDI outperforms existing methods on benchmark datasets for DDI prediction.
- The cross-view contrastive learning strategy improves representation learning by aligning molecular graphs and SMILES sequences.
- The proposed framework is robust and provides interpretable predictions.

## 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.