Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.

• Neural tangent kernel is used to construct the similarity matrices. • Correntropy-loss function is introduced into matrix factorization. • An efficient iterative algorithm is employed to optimize the model. Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance. [ABSTRACT FROM AUTHOR]