RT Journal Article
SR Electronic
T1 A Machine Learning approach for assessing drug development risk
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.08.331926
DO 10.1101/2020.10.08.331926
A1 Vangelis Vergetis
A1 Gerasimos Liaropoulos
A1 Maria Georganaki
A1 Andreas Dimakakos
A1 Dimitrios Skaltsas
A1 Vassilis G Gorgoulis
A1 Aristotelis Tsirigos
YR 2020
UL http://biorxiv.org/content/early/2020/10/09/2020.10.08.331926.abstract
AB 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.Competing Interest StatementVV, GL, MG, AD and DS are employees of Intelligencia.AI. AT is a scientific advisor to Intelligencia.AI.