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Machine learning in the biopharma industry

Modern high-throughput technologies deployed in R&D for new health products have opened the door to Machine Learning applications that allow the automation of tasks and support for data-driven risk-based decision making. Appealing opportunities of applying Machine Learning appear for the development of modern complex drugs, for biomanufacturing production lines optimization, or even for elaborating product portfolio strategies. Nevertheless, many practical challenges make it difficult to apply Machine Learning models in the biopharmaceutical field. Innovative approaches must thus be considered in many of these practical cases. This tutorial paper is an attempt to describe the landscape of Machine Learning application to the biopharmaceutical industry along three dimensions: opportunities, specificities or constraints and methods.