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A Machine Learning approach for assessing drug development risk

Vangelis Vergetis, Gerasimos Liaropoulos, Maria Georganaki, Andreas Dimakakos, Dimitrios Skaltsas, Vassilis G Gorgoulis, Aristotelis Tsirigos
doi: https://doi.org/10.1101/2020.10.08.331926
Vangelis Vergetis
1Intelligencia Inc., New York, NY, USA
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Gerasimos Liaropoulos
1Intelligencia Inc., New York, NY, USA
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Maria Georganaki
1Intelligencia Inc., New York, NY, USA
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Andreas Dimakakos
1Intelligencia Inc., New York, NY, USA
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Dimitrios Skaltsas
1Intelligencia Inc., New York, NY, USA
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Vassilis G Gorgoulis
2Molecular Carcinogenesis Group, Department of Histology and Embryology, Faculty of Medicine, School of Health Sciences, National Kapodistrian University of Athens, Greece
3Biomedical Research Foundation, Academy of Athens, Athens, Greece
4Molecular and Clinical Cancer Sciences, Manchester Cancer Research Centre, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
5Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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Aristotelis Tsirigos
6Institute for Computational Medicine, New York University School of Medicine, New York, NY, USA
7Department of Pathology, New York University School of Medicine, New York, NY, USA
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  • For correspondence: aristotelis.tsirigos@nyulangone.org
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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.

Competing Interest Statement

VV, GL, MG, AD and DS are employees of Intelligencia.AI. AT is a scientific advisor to Intelligencia.AI.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 09, 2020.
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A Machine Learning approach for assessing drug development risk
Vangelis Vergetis, Gerasimos Liaropoulos, Maria Georganaki, Andreas Dimakakos, Dimitrios Skaltsas, Vassilis G Gorgoulis, Aristotelis Tsirigos
bioRxiv 2020.10.08.331926; doi: https://doi.org/10.1101/2020.10.08.331926
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A Machine Learning approach for assessing drug development risk
Vangelis Vergetis, Gerasimos Liaropoulos, Maria Georganaki, Andreas Dimakakos, Dimitrios Skaltsas, Vassilis G Gorgoulis, Aristotelis Tsirigos
bioRxiv 2020.10.08.331926; doi: https://doi.org/10.1101/2020.10.08.331926

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