Combination therapy synergism prediction for virus treatment using machine learning models.

ABSTRACT: Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models sho

SUBMITTER: Majidifar S 

PROVIDER: S-EPMC11373828 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature