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

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

Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.

Molecular Fragment-Based Graph Isomorphism Networks for Interpretable Prediction of Synergistic Drug Combinations

Drug combination therapy plays an increasingly important role in the clinical treatment of complex diseases, such as cancer, as rational drug combinations can enhance therapeutic efficacy and reduce toxic side effects. However, existing methods still exhibit limitations in the granularity of drug molecular representation, drug interaction modeling, and cell line context awareness, which restrict further improvements in predictive performance. To address these issues, we propose FragSyn, a deep graph learning framework for predicting synergistic drug combinations based on molecular fragmentations. FragSyn first decomposes drug molecules into chemically meaningful fragments according to breaks of retrosynthetically interesting chemical substructure rules and learns fragment-level molecular representations through a graph isomorphism network with edge features. It then captures nonlinear relationships between drug pairs from multiple perspectives while introducing a gating modulation mechanism conditioned on cell line features, enabling drug representations to adapt dynamically to the cell line context. Finally, multisource features are fused to perform binary classification of synergy versus antagonism. FragSyn achieves AUC, AUPR, and ACC of 0.944, 0.942, and 0.872, respectively, outperforming eight baseline models, and demonstrates optimal generalization performance in both leave-one-out cross-validation and external validation. Ablation studies and interpretability analyses further validate the rationality of FragSyn and its ability to identify key fragments. These results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.

IHLO-DTI: Drug-Target Interaction Prediction Based on Improved Hypergraph Neural Network and Laplacian Matrix Optimization

IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.

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

LOGIC: LLM-Driven Cross-Scale Feature Coupling for Drug-Disease Interaction Prediction.

The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional domain knowledge.

A Novel Graph Transformer Framework for Predicting Drug-Disease Associations with Structural Awareness.

SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.