PT  - JOURNAL ARTICLE
AU  - Vergetis, Vangelis
AU  - Liaropoulos, Gerasimos
AU  - Georganaki, Maria
AU  - Dimakakos, Andreas
AU  - Skaltsas, Dimitrios
AU  - Gorgoulis, Vassilis G
AU  - Tsirigos, Aristotelis
TI  - A Machine Learning approach for assessing drug development risk
AID  - 10.1101/2020.10.08.331926
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.08.331926
4099  - http://biorxiv.org/content/early/2020/10/09/2020.10.08.331926.short
4100  - http://biorxiv.org/content/early/2020/10/09/2020.10.08.331926.full
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.