Accelerating life science + drug development with explainable AI

Clients we are proud to disclose

Our proprietary, explainable AI — combined with our expertise in bioinformatics and machine learning — gives life science and pharma companies the explainable edge.

Are you caught in the knowledge gap between your data and your next discovery?

Abzu’s proprietary, explainable AI and expertise in health analytics and machine learning augment your team with novel insights.

Artificial Intelligence brain on fire

Artificial intelligence

A new class of AI to accelerate research: The QLattice®

Scientific ideas


World-class bioinformaticians, systems biology, and data scientists

Pharma researcher using AI

Scientific knowhow

Health science, biophysics, mathematics, and statistics experts

Explainable insights per project or throughout the pharma value chain

Augment your team with novel ideas. Get the explainable edge in developing new therapies.

Target selection

Abzu in the pharma value chain

Lead discovery

Abzu in the pharma value chain

Lead optimisation

Abzu in the pharma value chain


Abzu in the pharma value chain


Abzu in the pharma value chain

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.

What people are saying

Advancing scientific research

Publications + preprints

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).

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

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).

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).

Explainable long-term building energy consumption prediction using QLattice

Simon Wenninger, Can Kaymakci, Christian Wiethe. Applied Energy 308, 0306-2619 (2022).

Symbolic regression outperforms other models for small data sets

Casper Wilstrup, Jaan Kasak

Self-organized and self-managed

We don’t have any bosses. Abzoids determine their own salaries and schedules and make decisions about their work priorities. This requires transparency and trust, which is an integral part of our technology and who we are.

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