TARGET IDENTIFICATION

Find new druggable targets with multi-omics data

Digital drug design

PRODUCTS + SERVICES >

CASE STUDIES >

TESTIMONIALS >

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

Ready your data for analysis, perform extensive quality controls, and enhance your research with biobank access.

Predictive modeling

Discover hard-to-find relationships and synergistic gene pairs backed by scientific evidence and explainable models.

Pathway analysis + clustering

Explain how targets affect relevant pathways for your disease or outcome with public knowledge bases and evidence.

Target ranking + nomination

Rank and score genes and gene pairs with custom metrics like accessibility, druggability, genetics, safety, and novelty.
LET'S TALK >

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.

Leverage all of your omics data. The target search can include data of different biomolecular levels from genomics to phenotypes.
Contera Pharma
Abzu's explainable artificial intelligence technology and in-house RNA and data science capabilities, combined with their service-based business model, is a key stepping stone for executing our R&D strategy.

Kenneth Vielsted Christensen,

CSO

Roche
Although Abzu's QLattice (and its collaborative scientists) have helped improve the performance of our drug design algorithms, it's the explainability of the QLattice models that is — in our setting — the most valuable feature for us.

Morten Lindow,

Therapeutic Modalities

dr.evidence
Hypotheses generated by Abzu's explainable AI, the QLattice, are brilliantly put into context with our knowledge graphs. I believe a combined setup like ours is a very realistic picture of the future of data science.

Umut Eser,

CIO

Roche
While we consider ourselves world-leading in the computational design of oligonucleotide-based therapies, our collaboration with Abzu has added new aspects when applying machine learning to drug discovery data.

Morten Lindow,

Therapeutic Modalities

Dogodan Therapeutics
Abzu's QLattice helps determine the precise genomic locations for controlling transcription factor binding and modulating gene expression of a given target. This is a major step forward for genomic medicines.

Morten Lindow,

Therapeutic Modalities

Case studies

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.

READ MORE >

READ THE REPORT >

Abzu is a Gartner Cool Vendor 2022

Publications + preprints

Abzu published in Bioinformatics

Identifying interactions in omics data for clinical biomarker discovery using symbolic regression

Niels Johan Christensen, Samuel Demharter, Meera Machado, Lykke Pedersen, Marco Salvatore, Valdemar Stentoft-Hansen, Miquel Triana Iglesias. Bioinformatics (2022).

READ MORE >

BMC Medical Informatics and Decision Making

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

Wilstrup, C., Cave, C. BMC Medical Informatics Decision Making 22, 196 (2022).

READ MORE >

American Journal of Obstetrics and Gynecology

Explainable “White-box” machine learning is the way forward in pre-eclampsia screening

Michael Christiansen, MD, FRCPath, Casper Wilstrup, Paula L. Hedley, PhD, MPH. American Journal of Obstetrics and Gynecology, S0002-9378 (2022).

READ MORE >

Abzu medRxiv preprint

Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

Casper Wilstrup, Paula L. Hedley, Line Rode, Sophie Placing, Karen R. Wøjdemann, Anne-Cathrine Shalmi, Karin Sundberg, Michael Christiansen

READ MORE >

Resarch paper: The QLattice outperfroms other machine learning methods

Symbolic regression outperforms other models for small data sets

Casper Wilstrup, Jaan Kasak

READ MORE >

Contact Abzu

It’s easy to get in touch with an Abzoid.

Contact form

Fill out the form below, and an Abzoid will be in touch in 24 hours.

Email us

Contact Sales at sales@abzu.ai or a scientist at science@abzu.ai.

Call us

Reach us during standard CET business hours at +45 31 23 47 64.