Research on Personalized Medicine Published by Researchers at University of North Carolina Chapel Hill (Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods).

Researchers at the University of North Carolina Chapel Hill have conducted a study on personalized medicine, focusing on the integration of drug molecular representations into predictive models for Drug Response Prediction (DRP). The study found that incorporating molecular representations from PubChem fingerprints or SMILES can enhance the performance of DRPs when used with deep learning models. The research provides insights on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, which can aid in drug repurposing, personalized medicine, and new drug discovery. The study was supported by Nih/nlm and published in Scientific Reports. [Extracted from the article]