GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.

ABSTRACT: The prediction of virus-drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the

SUBMITTER: Jin Y 

PROVIDER: S-EPMC11924387 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature