Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[Prediction and verification of therapeutic drugs for triple-negative breast cancer using a knowledge graph-based drug repurposing model].
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Wu Diheng, Xu Zhanfa, Li Yi, Zhang Mingxu, Lin Jiaze, Lü Yijun, Guan Daogang, Qin Genggeng
Аннотация
OBJECTIVES: To construct a knowledge graph-based drug repurposing model for predicting potential therapeutic drugs for triple-negative breast cancer (TNBC).
METHODS: Drug-target interaction (DTI) affinity data were collected from the BindingDB database and filtered (including data of Kd, Ki, EC50 and IC50). The proposed KGNN model integrates graph convolutional network (GCN)‑extracted drug molecular graph features, ProtBERT-pretrained protein sequence representations, and STRING-derived protein-protein interaction (PPI) knowledge graphs. Multi-head attention mechanisms and gated fusion modules were used to model interaction dependencies. Model performance was evaluated using mean squared error (MSE), Pearson correlation coefficient (PCC), and concordance index (CI). Ablation studies were performed to assess module contributions, and cold-start experiments were conducted to test generalization ability of the model. Using data from TCGA, 1340 TNBC-associated pathogenic genes were screened by bioinformatics analyses and mapped to targets using UniProt. KGNN was applied to predict the candidate drugs, which were validated through molecular docking and molecular dynamics simulations.
RESULTS: In the DTI affinity prediction task, KGNN outperformed the benchmark models including KronRLS, SimBoost, DeepDTA, FusionDTA, and GraphDTA (MSE=3.2697, PCC=0.8037, and CI=0.7862). Ablation studies confirmed the critical roles of the modules for enhancing model performance (multi-head attention increased MSE by 5.60%; PPI fusion increased MSE by 10.82%). In cold-start scenarios, KGNN maintained superior performance over the comparators in unseen drug/target settings, demonstrating robust generalization. The TNBC candidate drug predictions well aligned with docking affinities and dynamics simulations (Pearson correlation coefficient>0.85), while attention visualization highlighted the efficacy hotspots.
CONCLUSIONS: The KGNN model can effectively predict drug-target interactions to facilitate drug repurposing and the design of multi-target drugs while reducing the screening space and experimental validation costs.
METHODS: Drug-target interaction (DTI) affinity data were collected from the BindingDB database and filtered (including data of Kd, Ki, EC50 and IC50). The proposed KGNN model integrates graph convolutional network (GCN)‑extracted drug molecular graph features, ProtBERT-pretrained protein sequence representations, and STRING-derived protein-protein interaction (PPI) knowledge graphs. Multi-head attention mechanisms and gated fusion modules were used to model interaction dependencies. Model performance was evaluated using mean squared error (MSE), Pearson correlation coefficient (PCC), and concordance index (CI). Ablation studies were performed to assess module contributions, and cold-start experiments were conducted to test generalization ability of the model. Using data from TCGA, 1340 TNBC-associated pathogenic genes were screened by bioinformatics analyses and mapped to targets using UniProt. KGNN was applied to predict the candidate drugs, which were validated through molecular docking and molecular dynamics simulations.
RESULTS: In the DTI affinity prediction task, KGNN outperformed the benchmark models including KronRLS, SimBoost, DeepDTA, FusionDTA, and GraphDTA (MSE=3.2697, PCC=0.8037, and CI=0.7862). Ablation studies confirmed the critical roles of the modules for enhancing model performance (multi-head attention increased MSE by 5.60%; PPI fusion increased MSE by 10.82%). In cold-start scenarios, KGNN maintained superior performance over the comparators in unseen drug/target settings, demonstrating robust generalization. The TNBC candidate drug predictions well aligned with docking affinities and dynamics simulations (Pearson correlation coefficient>0.85), while attention visualization highlighted the efficacy hotspots.
CONCLUSIONS: The KGNN model can effectively predict drug-target interactions to facilitate drug repurposing and the design of multi-target drugs while reducing the screening space and experimental validation costs.