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.
HSSynergy, a hierarchical substructure-aware deep learning framework for predicting anticancer drug synergy, 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 cr...
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 u...
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
