bioRxivBioinformaticsPreprint
A Machine Learning approach for assessing drug development risk
Vangelis Vergetis, Gerasimos Liaropoulos, Maria Georganaki, Andreas DimakakosiD, Dimitrios Skaltsas, Vassilis G. GorgoulisiD, Aristotelis TsirigosiD
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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