VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction

Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy-using more than one drug for treatment-can enhance efficacy and minimize the side effects associated with individual drugs. However, there is currently limited research focused on computational approaches to combination therapy for viral diseases. This paper proposes AI-based models to predict novel drug combinations that can synergistically treat viral diseases. To achieve this, we have compiled a comprehensive dataset containing information on viruses, drug compounds, and their approved interactions. We introduce two attention-based models and compare their performance with traditional machine learning and deep learning models in predicting synergistic drug pairs for treating viral diseases. Among all the methods tested, the random forest algorithm and one of the attention-based models utilizing a customized dot product as a predictor showed the highest performance. Notably, two predicted combinations-acyclovir + ribavirin and acyclovir + Pranobex Inosine-have been experimentally validated to produce a synergistic antiviral effect against the herpes simplex virus type 1, as reported in existing literature.