Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.08.331926v1?rss=1 Authors: Vergetis, V., Liaropoulos, G., Georganaki, M., Dimakakos, A., Skaltsas, D., Gorgoulis, V. G., Tsirigos, A. Abstract: Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, 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 inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources, and, as a result, 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. Copy rights belong to original authors. Visit the link for more info