Machine and deep learning approaches for cancer drug repurposing

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.

Keywords: Artificial intelligence; Deep learning; Drug discovery; Drug repurposing; Machine learning.

Figure 1.. Overview of structure-based machine learning strategies for identification of drug-target interactions.

(A) Docking of small molecules into binding sites of three-dimensional protein target models. (B) Cheminformatics methods (i.e. QSAR) where chemical descriptors of small molecules are leveraged to create models relating descriptors to quantifiable target binding biological endpoints (i.e. binding vs non-binding, IC50, etc.). (C) Proteochemometrics similar to (B) but with additional descriptors derived from the protein target such as amino acid sequence.

Figure 2.. Schematic of machine learning applied to (A) phenotypic and (B) transcriptomic data.

Bioassay data can be obtained from numerous publicly available databases such as PubChem, ChEMBL, TCGA and L1000 and implemented into machine learning systems.