TARGET IDENTIFICATION
Find new druggable targets with multi-omics data

Understand your targets and their effects
Reveal target-disease relationships and identify novel druggable targets using our state-of-the-art computational target identification platform and services.
Flexible services to support your team
Data access and processing
Predictive modeling
Pathway analysis + clustering
Target ranking + nomination
Why Abzu?

Expand your disease understanding by investigating the underlying biological processes that explain your data.
Reduce thousands of potential targets to a highly curated set, supported by reports and analyses, to find safer, druggable targets.

Kenneth Vielsted Christensen,
CSO

Morten Lindow,
Therapeutic Modalities

Umut Eser,
CIO

Morten Lindow,
Therapeutic Modalities

Morten Lindow,
Therapeutic Modalities
Case studies

Techtopia podcast: Danish AI against breast cancer
Just because you can predict what’s going to happen does not mean you have an explanation for the phenomenon.

Multi-omics analysis made easy: A breast cancer example
The QLattice, a new explainable AI algorithm, can cut through the noise of omics data sets and point to the most relevant inputs and models.

Accelerating scientific discoveries with explainable AI: A breast cancer example
A 17 minute video about Abzu’s origins and an impactful application in life science.
Abzu recognized as a Cool Vendor in artificial intelligence
Abzu is named a “Cool Vendor” in the 2022 Gartner® “AI Governance and Responsible AI — From Principles to Practice” report.
Contact Abzu
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Email us
Contact Sales at sales@abzu.ai or a scientist at science@abzu.ai.