ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing

Drug repurposing efficiently identifies new applications for already approved drugs at reduced time and cost. ConvAHKG, an action-based hybrid knowledge graph approach, is proposed to improve the prediction of drug-disease associations by leveraging biological relationships among drugs, proteins, and diseases. AHKG is designed to integrate both drug and disease features to provide a comprehensive framework. To represent these relationships, Word2Vec embeddings are used to capture the semantic similarities among entities, and a novel dual-channel 1D convolutional neural network (IDC_Conv1D) is introduced for the classification of drug-disease pairs. This architecture is specifically intended to handle the complexity and heterogeneity of biological data. Furthermore, to address the significant class imbalance present in drug-disease datasets, a weighted binary cross-entropy loss function was introduced that assigns higher penalties to minority-class misclassifications, resulting in improved predictive performance. ConvAHKG outperforms state-of-the-art models, with an AUC of 0.9836 and an AUPRC of 0.9686. To validate its practical utility, we applied ConvAHKG to study non-small cell lung cancer (NSCLC). The framework identified promising therapeutic candidates for NSCLC, including Trastuzumab, and molecular docking analyses demonstrated strong binding interactions for an additional predicted but experimentally unvalidated compound, further supporting its potential as a novel treatment option. All data and code used in this study are available at https://github.com/Marzieh-Khodadadi/ConvAHKG .