M²DDI: A Unified Framework for Dynamic Multimodal Fusion in Drug-Drug Interaction Prediction

Advances in multimodal data integration for drug efficacy prediction: Methodological evolution and clinical translation.

Drug efficacy prediction remains a cornerstone of drug development and precision therapy. However, integrating heterogeneous biomedical data, including multi-omics profiles, pathological imaging, electronic health records, and pharmacokinetic-pharmacodynamic (PK/PD) time-series, faces three fundamental barriers, namely cross-domain distribution shifts between preclinical and clinical data, relational mismatches between isolated vector representations and biological networks, and feature heterogeneity across disparate modalities. To address these challenges, three AI paradigms have emerged, transfer learning for cross-domain alignment, graph neural networks for structured relational modeling, and Transformers for global cross-modal feature interaction. Importantly, these techniques form a many-to-many complementary system rather than a one-to-one correspondence, a key insight that this review explicitly formalizes. We further elaborate encoding workflows for PK/PD data to bridge static molecular signatures with dynamic in vivo exposure trajectories. Four graded clinical applications are outlined, including personalized monotherapy, combination optimization, drug repurposing, and preclinical-to-clinical evaluation of novel candidates. We also dissect persistent bottlenecks such as data harmonization, model interpretability, and prospective validation, and propose five actionable directions, namely privacy-preserving benchmarks, causally interpretable models, temporal dynamic frameworks, cross-domain generalization, and lightweight clinical tools. By integrating theoretical rationales, methodological synergies, and hierarchical translational scenarios, this review provides a unified roadmap to accelerate the clinical deployment of multimodal drug response prediction.

MKASynergy: an adaptive method for drug synergy prediction via a mixture-of-experts kernel mechanism

MKASynergy, an adaptive drug synergy prediction method based on a mixture-of-experts kernel mechanism, achieves competitive predictive performance and visualization analysis confirms the model’s effectiveness in feature decoupling and helps interpret latent drug synergistic mechanisms.

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.

Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.

Robust Prediction of Drug Interactions using Chemical Descriptors.

Polypharmacy requires accurate prediction of drug-drug interactions to prevent adverse events, yet existing models often lack reliability and explainability. We propose T-DDI, a descriptor-based deep learning framework for multi-class drug-drug interaction prediction. Rather than relying on complex graph embeddings, T-DDI uses explicit physicochemical descriptors and an uncertainty-aware estimator to handle severe class imbalance. Evaluated on 868,069 drug pairs spanning 178 interaction types, T-DDI achieves a Macro F1 of 0.8452 on the held-out test set, improving to 0.8992 within the high-confidence subset (87.91% of test samples), outperforming all evaluated baselines within the architectures and datasets considered here. An illustrative prospective case-study assessment on five newly FDA-approved drugs from late 2025 showed that T-DDI can generate mechanistically plausible DDI hypotheses for compounds not used during model development. T-DDI pairs confidence-stratified predictions with LIME-based feature-level explanations and a web application for screening, supporting more reliable drug safety monitoring.

A Knowledge Graph-Driven Multimodal Framework for Drug-Disease Association Prediction.

Drug repurposing represents a cost-effective strategy to identify novel therapeutic applications for existing pharmaceuticals, circumventing the protracted timelines of traditional drug discovery. While knowledge graph (KG) based methods excel at integrating heterogeneous biomedical data, they often struggle to harmonize high-level domain knowledge with fine-grained molecular mechanisms. We propose KGDDA, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies. By leveraging an attention-driven fusion mechanism, KGDDA dynamically merges contextual topological embeddings with ontology-derived priors, enabling the adaptive capture of intricate drug-disease interactions. Extensive evaluations on two benchmark datasets demonstrate that KGDDA consistently outperforms state-of-the-art baselines in both predictive accuracy and generalization. Furthermore, case studies on head and neck cancer and small cell lung cancer validate KGDDA's ability to provide actionable mechanistic insights, highlighting its potential to accelerate therapeutic discovery and precision medicine.