همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Combination therapy synergism prediction for virus treatment using machine learning models

نویسندگان :

Shayan Majidifar¹ Arash Zabihian² Mohsen Hooshmand³

1- IASBS 2- Department of QA, Kimia Zist Parsian Pharmaceutical Company, Zanjan, Iran 3- IASBS

کلمات کلیدی :

Viral disease treatment،Combination therpay،deep learning،machine learning،drug repurposing

چکیده :

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 showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.