Machine learning applications in drug development

Graphical abstract

Highlights

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  Applications of sequential learning and recommender systems to pharmaceutics.

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  Review of Machine Learning methods in drug discovery, testing and repurposing.

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  Survey of available genomic data and feature selection methods for drug development.

Abstract

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.