Abzu's research in pharma R&D and AI.
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.
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:
To design drugs in-silico which make it successfully to the clinical phase twice as often and at half the cost as today.
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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.
Let’s apply our repositioned selves and the QLattice in a very hot area of research: nucleic acid-based medicines. One particular type, RNA interference (RNAi), involves a process by which specific mRNAs are targeted and degraded.
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.
Abzu researches into the best way to design antisense oligonucleotides (ASOs), including what features contribute to toxicity.
Data sciencey-sphere, I have big news: A radical new machine learning model has surfaced.
Opening the black of AI with explainable AI (the QLattice). What does an explainable model look like? What does a plot look like?
Do you think about machine learning? How about all the research put into self-driving cars or image recognition or natural language processing?
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