Abzu's research in drug discovery.
Pioneering the path to in vivo success by better understanding activity, stability, safety, and delivery.
Abzu's research predicting and understanding in vivo effectiveness.
We design drugs that are great in theory — and in practice.
Abzu’s pioneering research addresses the gap between in vitro and in vivo. Abzu’s research shortens development timelines and reduces the failure rate in drug discovery by revealing the “why” behind drug and disease mechanisms.
So dive into the heart of innovation with Abzu’s cutting-edge research. You’ll discover how we’re accelerating the path to real-world therapeutics with insights driven by our explainable AI.
Abzu's research mission:
By 2025, Abzu will be able to design drugs which make it successfully to the clinical phase twice as often and at half the cost as today.
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Or simply browse all Abzu research:
Here we use the QLattice® to generate siRNA activity models from publicly available data to create insights that can be used to design active siRNAs.
An example of peptide drug development: Featurization and modeling using anticancer peptides.
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.
Our results-driven world pressures us to give up good science.
Curing your data preprocessing blues: Automatic handling of categorical data and scaling
The increasing application of black-box models gives rise to a range of both ethical and scientific questions.
How the QLattice explains the cellular toxicity of RNA-targeting drugs.
Data sciencey-sphere, I have big news. A radical new machine learning model has surfaced.
Explainable AI with the QLattice and feynplots
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