Data-Driven Target Selection
Enabling a data-driven, actionable approach to target discovery, indication expansion and target assessment
Maximise preclinical confidence in target efficacy and safety
With over 70% of failures in the clinical stage attributed to the choice of drug target, preclinical target selection is the most impactful step in drug development. We collaborate with biopharma companies to address this issue by establishing a data-driven, actionable target selection process that maximizes preclinical confidence in target efficacy and safety.
"We worked with Euretos to leverage their AI capabilities with the aim of finding new targets in two different indications. It was a real pleasure to work with them and the whole process was characterized by transparency, their willingness to listen to our needs and suggestions, and swift responses and data delivery. In addition, we found their platform very useful for getting an overview of the different targets and for general queries."
Tau Benned-Jensen, Senior Research Scientist, H. Lundbeck A/S
This ebook covers the following:
-How computational disease models predict novel gene-disease associations.
-A case study: Target Discovery for Rheumatoid Arthritis
-The benefits of using an AI-integrated knowledge graph for target assessment
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