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A Machine Learning approach for assessing drug development risk
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
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By Multimodal LLCLink 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