Cutting Edge Deep Learning Framework Used to Predict Drug-Drug and Drug-Target Interactions
A new deep learning framework has been developed that can accurately predict drug-drug interactions and drug-target interactions. This framework, named DeepDDI, was developed by researchers at the University of Minnesota and aims to provide powerful insights into drug-drug and drug-target interactions. DeepDDI is based on a deep learning network architecture that combines the prediction power of Convolutional Neural Networks and Recurrent Neural Networks. This deep learning network is used to integrate information from a wide variety of sources, including drug-drug interaction databases, biological databases, and drug-target interaction databases. By integrating information from all these sources, DeepDDI can accurately predict drug-drug interactions and drug-target interactions. The results of this research are promising and may eventually lead to better understanding of drug-drug and drug-target interactions, and ultimately, better treatment options for patients.
source: Phys.org