Deep learning improves prediction of drug–drug and drug–food interactions

By Jae Yong Ryu, Hyun Uk Kim, Sang Yup Lee. Proceedings of the National Academy of Sciences of the United States of America, 2018

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Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “all, a given drug pair manifests clinically within a patient population [45, 302, 303].”
An Introduction to Machine Learning (2020, background): “trained a deep neural network on a large curated database covering 192,284 drug-drug interactions in order to predict drug-drug and drugfood interactions for prescriptions, dietary recommendations, and new molecules.(64) Combining datasets from multiple studies to create large databases increases th”
Graph embedding on biomedical networks: methods, applications and evaluations (2019, unknown): “DeepDDI first adopts principal component analysis to reduce the dimension of the drug features (i.e. drug substructure) and then feeds these into a deep neural network (DNN) classifier.”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “…agents was recently published in the literature – the authors developed a computational platform, DeepDDI, for better understanding of drug– drug interactions and associated mechanisms and prediction of alternative drugs for intended clinical use without negative health effects [65].”