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bioRxiv · 10.1101/2020.10.08.331926

A Machine Learning approach for assessing drug development risk

Vergetis, V.·Liaropoulos, G.·Georganaki, M.·Dimakakos, A.·Skaltsas, D.·Gorgoulis, V. G.·Tsirigos, A.
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Abstract

Characterizing drug development risk - the probability that a drug will eventually receive regulatory approval - has been notoriously hard given the complexities of drug biology and clinical trials. This often leads to an inefficient allocation of resources, and an overall reduction in R&D productivity. We propose a Machine Learning (ML) approach that provides a more accurate and unbiased estimate of drug development risk than traditional models.

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Vergetis, V., Liaropoulos, G., Georganaki, M., Dimakakos, A., Skaltsas, D., Gorgoulis, V. G., Tsirigos, A.. 2020-10-09. A Machine Learning approach for assessing drug development risk. https://doi.org/10.1101/2020.10.08.331926

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