Title:The Next Generation of Machine Learning in DDIs Prediction
Volume: 27
Issue: 23
Author(s): Wei Huang, Chunyan Li*, Ying Ju and Yan Gao
Affiliation:
- Yunnan Minzu University, Kunming,China
Keywords:
Drug, drug-drug interactions, deep learning, machine learning, computational methods, biomedical informatics.
Abstract: Drug-drug interactions may occur when combining two or more drugs may cause some adverse
events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers
who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to
find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs
is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to
find all the adverse reactions through drug experiments. Therefore, new attempts have been made in using
computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying
the computational method for predicting drug-drug interactions. We first briefly introduce the widely
used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions
prediction. We also discussed the challenges and opportunities of applying the computational method in
drug-drug interactions prediction.