Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

ABSTRACT: High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on th

SUBMITTER: Spath J 

PROVIDER: S-EPMC10864927 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature