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.

近年来，关于癌症发生、进展和转移的基础知识呈指数级增长。先进的“组学”与机器学习和人工智能（深度学习）方法相结合，有助于阐明对那些可能适合药物调节的过程至关重要的目标和途径。然而，目前的抗癌治疗设备仍然落后。由于开发一种新药的成本仍然高得令人望而却步，鉴于已知的安全性和降低成本壁垒，人们寻求重新利用现有的已批准和研究药物。值得注意的是，肿瘤药物再利用的成功并不常见。计算机计算已经制定了一些策略来帮助对生物学过程进行建模，以找到新的疾病相关靶点并发现新的药物靶点和药物表型关联。机器和深度学习方法尤其使这些成功实现了飞跃。这篇综述将讨论这些方法，因为它们与癌症生物学以及肿瘤疾病中药物再利用机会的免疫调节有关。