A Machine Learning approach for assessing drug development risk
Vangelis Vergetis, Gerasimos Liaropoulos, Maria Georganaki, Andreas Dimakakos, Dimitrios Skaltsas, Vassilis G. Gorgoulis, Aristotelis Tsirigos · bioRxiv (Cold Spring Harbor Laboratory) · 2020
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.