Machine and deep learning approaches for cancer drug repurposing. (January 2021)

• Record Type: Journal Article
• Authors: Issa, Naiem T.; Stathias, Vasileios; Schürer, Stephan; Dakshanamurthy, Sivanesan
• Is Part Of: Seminars in cancer biology. Volume 68(2021)
• Page Start: 132
• Page End: 142
• Publication Date: 2021-01
• Subjects: ADE adverse drug event -- ADMET absorption, distribution, metabolism, excretion, toxicity -- CNN convolutional neural network -- DNN deep neural network -- EHR electronic health record -- EMT epithelial-mesenchymal transition -- ICI immune checkpoint inhibitor -- MDSC myeloid-derived suppressor cells -- NLP natural language processing -- PCM proteochemometric -- QSAR quantitative structure-activity relationship -- RF Random Forest -- SVM Support Vector Machine -- TCGA The Cancer Genome Atlas -- TIL tumor immune cell infiltrates; Drug repurposing -- Drug discovery -- Machine learning -- Deep learning -- Artificial intelligence; Cancer -- Periodicals; Neoplasms -- Periodicals; Review Literature; Cancer -- Périodiques; Electronic journals; 616.994
• DOI: 10.1016/j.semcancer.2019.12.011
• Languages: English
• ISSNs: 1044-579X

Abstract

Abstract: Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.