DDI-MMAF: Multi-modal affine fusion of visual and semantic representations for anticancer drug synergy prediction.

HSAF-DDI: Heterogeneous Semantic-Aware Drug-Drug Interaction Prediction with Hierarchical Adaptive Fusion.

Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.

SynerMolNet: Molecular Representation and Attention Fusion for Cancer Drug Synergy Prediction

Multimodal contrastive learning for integrating molecular representations and cellular phenotypes in drug-target interaction prediction

Abstract Motivation Accurate prediction of drug-target interactions (DTIs) is fundamental to drug discovery and mechanistic understanding. While deep learning has advanced computational DTI prediction, most existing methods rely primarily on molecular structural representations, including drug structures and protein sequences, while overlooking cellular phenotypes that reflect downstream biological effects. Cell Painting enables high-content morphological profiling that captures systems-level responses to chemical and genetic perturbations but remains underutilized in DTI modeling. Integrating molecular information with cellular phenotypes offers an opportunity to improve both predictive performance and biological interpretability. Results We propose a two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space. Stage 1 learns modality-specific representations independently from structure-based and image-based data; Stage 2 aligns these via multi-positive contrastive learning to bridge molecular structural information with cellular phenotypes. Cross-modal retrieval achieves median Recall@10 values of 0.77 (random split) and 0.33 (scaffold split), outperforming bilinear and random baselines. In external DTI prediction on the BIOSNAP dataset, our model achieves an AUC of 0.92 with image-based representations and 0.90 under structure-only settings, surpassing existing methods. Model interpretation via integrated gradients reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms. Availability https://github.com/YJRubyLai/Unified-DTI

TextDTI: A Multimodal Context Representation Learning Framework for Drug-Target Interaction Prediction.

This paper proposes TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations and enhances feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction.

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

IDEAL (Interpretability-Driven Evolvable Attentive Learning for Microbe-Drug Association) is proposed, a multi-view framework that integrates drug network topological attributes, BERT-encoded drug semantics, drug fingerprints, microbe genome sequence attributes, BERT-encoded microbe semantics, and microbe metabolic pathway attributes.

CMAF-DDI: A Knowledge-Enhanced Cross-Modal Fusion Method Leveraging Protein Representation for Multi-Class Drug-Drug Interactions.

Accurate prediction of drug-drug interactions (DDIs) is crucial for medication safety and personalized treatment. Most existing methods primarily exploit molecular graphs or biomedical knowledge graphs, while target protein sequence information is often underused. This paper proposes CMAF-DDI, a multi-class DDI prediction framework that integrates protein sequence features, molecular graph features, and knowledge graph features. CMAF-DDI contains a bi-level cross-modal fusion module: an Attention Fusion (AF) level that models global dependencies among modalities using multi-head attention, and a Triple-feature Product Fusion (TPF) level that captures high-order cross-modal co-activation after projecting all modalities into a shared latent space. Experimental results on DrugBank and DRKG show that CMAF-DDI improves multi-class DDI prediction compared with representative graph-based and multi-source fusion baselines. We further provide ablation, hyperparameter sensitivity, controlled protein perturbation, representation, and case-level analyses to examine the contribution and behavior of protein-enhanced cross-modal fusion.