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Cancer claims millions of lives yearly worldwide. While many therapies havebeen made available in recent years, by in large cancer remains unsolved.Exploiting computational predictive models to study and treat cancer holdsgreat promise in improving drug development and personalized design oftreatment plans, ultimately suppressing tumors, alleviating suffering, andprolonging lives of patients. A wave of recent papers demonstrates promisingresults in predicting cancer response to drug treatments while utilizing deeplearning methods. These papers investigate diverse data representations, neuralnetwork architectures, learning methodologies, and evaluations schemes.However, deciphering promising predominant and emerging trends is difficult dueto the variety of explored methods and lack of standardized framework forcomparing drug response prediction models. To obtain a comprehensive landscapeof deep learning methods, we conducted an extensive search and analysis of deeplearning models that predict the response to single drug treatments. A total of60 deep learning-based models have been curated and summary plots weregenerated. Based on the analysis, observable patterns and prevalence of methodshave been revealed. This review allows to better understand the current stateof the field and identify major challenges and promising solution paths.

Deep learning methods for drug response prediction in cancer: predominant and emerging trends