Jul 2026· International Conference on Big Data Computing Service and Applications· pp. 111-118· 0 citations· 39 references
Abstract
Identifying synergistic anti-cancer drug combinations is crucial for improving efficacy and reducing toxicity, but exhaustive experimental screening is prohibitively costly. We present SYNAPX, an explainable deep learning framework for drug synergy prediction that integrates chemical features of drug pairs with gene expression profiles of cancer cell lines. Our model combines ECFP6 fingerprints, physicochemical descriptors, toxicophore features, and transcriptomic features into a unified representation, and uses a fully connected neural network to predict continuous synergy scores. We further apply SHAP (SHapley Additive exPlanations) for biological interpretation to quantify feature contributions and explain individual predictions. Evaluated on the drug combination dataset of 23,052 oncology drug-combination samples, the method achieves strong predictive performance, including a ROC AUC of 0.9169, PR AUC of 0.7139, and balanced accuracy of 0.8289 after threshold optimization. Our explanation analysis shows that the most influential features are primarily molecular fingerprints and physicochemical descriptors, and a case study on the Methotrexate-BEZ-235 combination yields explanations consistent with known biological mechanisms.
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.
Lifeng Shao, Jianqiang Sun, Hong-Zhan Ma et al.· Journal of Chemical Informat...· 0 citations
DrGee is presented, an essentiality-centered platform that infers drug sensitivity solely from gene expression profiles, and the built-in DeepEEAA model integrates gene expression, gene essentiality, drug-protein affinity, and drug-gene associations to quantitatively predict IC50 values.
Hongtu Cui, Xiaohui Du, Hai-Xia Guo et al.· iScience· 0 citations
This study systematically benchmark six ranking loss functions, including state-of-the-art listwise methods, and five types of molecular representations across two large-scale drug screening datasets, CTRP and PRISM, to demonstrate that listwise loss functions such as LambdaLoss and LambdaRank consistently excel in both early and overall ranking quality.
Faraz Sarmeili, Benyamin Ghahremani-Nezhad, Mohammad Khalilpour et al.· PLoS ONE· 0 citations
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.
Cundong Lin, Jiancheng Ni, Ying Yang et al.· Network Modeling Analysis in...· 0 citations
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.
Unknown authors· Journal of Cheminformatics· 0 citations
T-DDI pairs confidence-stratified predictions with LIME-based feature-level explanations and a web application for screening, supporting more reliable drug safety monitoring, and outperforming all evaluated baselines within the architectures and datasets considered here.
Q. Kha, Duc-Quang-Anh Nguyen, Phi Pham Van Hoang et al.· npj Digital Medicine· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
