Reducing R&D risk for top-10 pharmaceutical company

Abzu improved the hit-rate and reduced R&D risk for a top-10 pharmaceutical company

We yielded a 44 percentage point improvement in catching toxic drugs in the drug development pipeline, reducing R&D risk and time.

Abzu yielded a 44 percentage point improvement in catching toxic drugs

Understanding what drives drug properties in conjunction with accurate drug activity predictions are clear advantages to improving drug candidate hit rate.

The customer: A top-10 pharmaceutical company

We supported a single Bioinformatician from a small bioinformatics department and Biologists and Chemists operating in a wet lab at an undisclosed top-10 pharmaceutical company.

Why Abzu?

Because traditional machine learning techniques don’t deliver enough actionable insights for scientists and researchers.

Technology + services

Abzu’s flexible packages include our proprietary technology, the QLattice®, and iterative lead generation and optimization.

The Abzoids

The Abzoids dedicated to this project were Casper Wilstrup, CEO, and Jaan Kasak, MSc in Theoretical and Mathematical Physics.

Revealing the “why” behind disease mechanisms and drug activity drastically reduces risk and timelines in drug development

Flexibility to suit your needs

Scientific understanding of drug properties of RNA therapeutics

Enhanced library design

Custom library of digital molecules for lead generation and optimization.
Abzu logo - xAI for RNATx

Explainable predictions

Iterative lead optimization with our experts in AI and RNA therapeutics.
Potential deliverable: A report withg findings about RNATx efficacy, toxicity, etc

Digital report

Progress, recommendations, and actions to develop safe and efficacious therapeutics.
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,


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

Statens Serum Institut - SSI
Collaborating with Abzu has led to highly significant new knowledge. Using the QLattice in combination with biomarkers, as well as medical and social register data, is a very promising approach to perosnalized medicine.

Michael Christiansen

Chief Physician

COV-IRT Covid International Research Team
Collaborating with Abzu helped identify quantifiable key drivers that led to discovering the key biomarkers involved with SARS-CoV-2 infection. Abzu was able to enrich our understanding and produce novel analyses.

Afshin Beheshti,


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