  Cancer claims millions of lives yearly worldwide. While many therapies have
been made available in recent years, by in large cancer remains unsolved.
Exploiting computational predictive models to study and treat cancer holds
great promise in improving drug development and personalized design of
treatment plans, ultimately suppressing tumors, alleviating suffering, and
prolonging lives of patients. A wave of recent papers demonstrates promising
results in predicting cancer response to drug treatments while utilizing deep
learning methods. These papers investigate diverse data representations, neural
network architectures, learning methodologies, and evaluations schemes.
However, deciphering promising predominant and emerging trends is difficult due
to the variety of explored methods and lack of standardized framework for
comparing drug response prediction models. To obtain a comprehensive landscape
of deep learning methods, we conducted an extensive search and analysis of deep
learning models that predict the response to single drug treatments. A total of
60 deep learning-based models have been curated and summary plots were
generated. Based on the analysis, observable patterns and prevalence of methods
have been revealed. This review allows to better understand the current state
of the field and identify major challenges and promising solution paths.
