Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients,
but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity
is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep
learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding
computational performance means that it has been used for many biomedical purposes, such as medical
image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted
anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress
regarding model performance and multi-omics data integration. However, deep learning is limited
by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach
for use in the anticancer drug screening process. Improving the performance of deep learning
models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug
sensitivity prediction and the use of deep learning in this research area. To provide a reference for future
research, we also review some common data sources and machine learning methods. Lastly, we discuss
the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding
this approach.
Predicting Macrocyclic Molecular Recognition with Machine Learning
