AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
A novel multiview feature fusion-based graph representation model (MFF-GRM) for predicting DDI that integrates drug molecular graphs, SMILES sequences, DDI information networks, and drug biological features to learn drug features more comprehensively.
Combination therapy offers a promising strategy for cancer treatment by reducing toxicity and overcoming drug resistance. However, existing substructure-based prediction methods may struggle to effectively capture the scale-specific features of substructure interactions and often overlook cell-line-specific substructure selection, which can limit mechanistic interpretability. To address this issue, we propose HSSynergy, a hierarchical substructure-aware deep learning framework for predicting anticancer drug synergy. It first employs Graph Attention-Convolution Fusion Module to adaptively extract multi-scale substructure features from molecular graphs. Rather than indiscriminately mixing features, it introduces a Scale-Aware Masked Attention mechanism that enforces precise layer-wise alignment, and utilizes hierarchical grouping with mask constraints to achieves same-scale focusing while shielding against cross-scale noise. Furthermore, shifting away from passive cell line representations, a Cell-Active Cross-Attention mechanism models the active selection of specific substructures by heterogeneous cancer cells, capturing precise drug-cell contexts. Rigorous evaluations on two benchmark datasets show that HSSynergy achieves superior performance compared to state-of-the-art methods with robust generalization to unseen drugs and cell lines. Beyond predictive metrics, it provides mechanistic interpretability insights, revealing the hierarchical emergence of substructures and accurately pinpointing literature-validated functional groups driving synergy in specific drug combinations. Notably, several novel synergistic combinations predicted by HSSynergy are supported by existing literature and clinical evidence, suggesting its potential utility in aiding anticancer drug discovery. HSSynergy (i) Scale-Aware Masked Attention restricts substructure interactions within scale groups to reduce cross-scale noise. (ii) Cell-Active Cross-Attention models dynamic, cell-specific substructure selection instead of static cell-line fusion. (iii)Hierarchical attention links synergy predictions to pharmacologically functional groups.
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.
Abstract Motivation Accurate prediction of synergistic drug combinations can accelerate anticancer combination discovery. Existing methods inadequately model higher order drug–drug–cell-line interactions and drug–disease associations and remain sensitive to sparse and noisy multiomics data, limiting generalization to unseen cell lines and drug combinations. Results We present Momentum Contrast (MoCo)-MultiSynergy, a multimodal framework that combines modality-specific momentum contrastive learning with heterogeneous hypergraph modeling. The hypergraph represents synergistic drug–drug–cell-line triplets and drug–disease associations, while gated residual propagation refines node representations. MoCo modules regularize encoded drug and cell-line representations using latent feature masking and Gaussian perturbation. On the O’Neil and NCI-ALMANAC datasets, MoCo-MultiSynergy achieves the highest AUROC and AUPRC across the evaluated settings, with the largest gains when generalizing to unseen cell lines and drug combinations. Availability and implementation Source code is available at https://github.com/27167199/MoCo-MultiSynergy.
A two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space, which reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms.
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