Unveiling Novel Gene-Drug-Disease Interactions with GDD-BERT

Traditional drug discovery is a long and costly process with a low success rate. Drug repurposing (DR) offers a promising alternative by leveraging existing drugs with known safety profiles. Network-based DR models have gained attention for their ability to integrate diverse types of biological and clinical data. However, effectively combining such heterogeneous data remains a significant challenge. We propose Gene-Drug-Disease-Bidirectional Encoder Representations from Transformers (GDD-BERT), an AI model designed to identify drug-gene-disease interactions, aiding drug repositioning. It utilizes BERTwalk, a novel embedding technique based on BERT, to generate vector representations of nodes in biological networks by analyzing complex relationships through random walks. These embeddings are combined into drug-gene-disease triplets, which are used in a classifier to predict potential interactions, such as a drug targeting a protein or a protein’s link to a disease. GDD-BERT efficiently reconstructs missing interactions and excels in sparse networks, outperforming traditional methods in discovering new therapeutic applications for drugs.