A machine learning framework for predicting drug-drug interactions.

ABSTRACT: Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug-drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug-drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l₂-regularized logistic regression model is built to predict drug-dru

SUBMITTER: Mei S 

PROVIDER: S-EPMC8413337 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature