Ignota Labs rescues promising but failing drugs, bringing new life to abandoned projects and new hope to patients.
Failure is the most valuable untapped dataset in biotech.
There is $100's of billions of data trapped behind drug failures - we're here to unlock its potential
More than half of all small molecule drugs which enter pre-clinical trials fail due to safety.
Discovering drugs that show therapeutic potential is difficult, time-consuming, and expensive. But of those found, it is harder still to find those drugs which do not also have severe side effects and can pass through clinical trials.
We don't just repurpose
drugs, we re-engineer
better versions.
Identify
Scalable process to find, analyse and assess failed and shelved assets
Solve
Create new chemistry and IP, optimise for clinical success
Develop & Sell
Develop to key inflection point then sell or partner
We are building a robust pipeline of matured assets at speed.
Failure doesn't need to be the end of the road. We look at historically failed clinical trials to identify the most promising targets as well as internal projects that were abandoned.
By focusing on safety problems that occur in Preclinical, Phase 1, or Phase 2 trials, we are able to identify the most promising drugs, turn them around quickly, and get them to patients faster than starting drug discovery from scratch.
SAFEPATH the world’s only clinical safety turnaround platform
SAFEPATH, applies deep learning to our combined bioinformatics and cheminformatics datasets to solve drug safety issues.
In preclinical and clinical studies, safety assessments typically reveal what went wrong—such as liver damage or heart issues—but fail to explain why these issues occurred, or how they might be mitigated.
SAFEPATH is a first-of-its-kind AI platform that combines advanced machine learning models with a multimodal data approach to enable a deep understanding of toxicity mechanisms, and offers actionable insights to facilitate drug turnaround.
CHEMINFORMATICS
A world-class suite of proteome-wide prediction models to identify what the drug binds to
BIOINFORMATICS
Understand the cause and effect from target to toxicity. Linking predicted off-targets to transcriptomic changes
CONTEXTUALISING TOOL
Score and prioritise hypotheses by surfacing evidence-based links within scientific journals
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