We improved the hit-rate and reduced R&D risk for a top-10 pharmaceutical company
Our explainable models yielded a 44 percentage point improvement in catching toxic drugs in the drug development pipeline, reducing R&D risk and time.
Simple and explainable models 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 services include our explainable AI, 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
Enhanced library design
Custom library of digital molecules for lead generation and optimization.
xAI modeling
Iterative lead optimization with our proprietary xAI and our experts in RNA therapeutics.
Digital report
Progress, recommendations, and actions to develop safe and efficacious therapeutics.
What people are saying…
Working with Abzu and using the QLattice to analyze our clinical data has provided us with new insights and helped us generate new hypotheses for exploring the potential for a biomarker-based enrichment strategy across cancer.
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.
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 personalised medicine.
Hypotheses generated by Abzu's explainable AI QLattice technology 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.
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.
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.
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 executing our R&D strategy.
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.
Looking for more?
event
Oligonucleotide Therapeutics Society
OTS is a forum to foster academic and industry-based research and development of oligonucleotide therapeutics.
Lykke Pedersen
September 27, 2022
careers
Bioinformatician / Data scientist
We are looking for a Bioinformatician or Data Scientist with omics- and / or clinical data experience in Barcelona.
Elyse Sims
September 27, 2022
careers
Bioinformatician / Data scientist
We are looking for a Bioinformatician or Data Scientist with omics- and / or clinical data experience in Copenhagen.
Elyse Sims
September 27, 2022
