Drug discovery is a bet. We improve the odds.
Qnite Labs uses AI to take risk out of the earliest decisions, where much of it is set.
The odds
How we improve them
Three questions, answered before the money is spent.
- Is the biology right?
- Is it safe and druggable?
- Is the molecule good enough?
Qortex
Qortex is our approach: Qs within Qs.
Every big question breaks into smaller ones, each handled by its own Q, which can break it down again. Answers flow back up and are checked at every level.
Well-tested answers, in less time.
Why it can work
Precision and speed, from three things the evidence already shows.
4% → 19%
Rigour pays
When AstraZeneca made its target and molecule checks stricter, success from candidate to the end of Phase III rose almost fivefold.3 Done by hand, that rigour is slow and costly. Qortex runs it on every decision.
R 0.01
No model is right everywhere
On one enzyme, a leading open AI model was no better than chance until it was retrained on project data.4 Qortex checks each answer across several models, flags where they disagree, and swaps in better ones as they arrive.
~13 months
The honest part
It is still a bet, for us too.
Read the benchmark →Talk to us
For partners and investors.
hello@qnitelabs.com- BIO, Informa Pharma Intelligence & QLS Advisors. Clinical Development Success Rates 2011–2020 (2021).
- Sun D. et al. Why 90% of clinical drug development fails and how to improve it. Acta Pharm Sin B (2022).
- Morgan P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat Rev Drug Discov (2018).
- Amini S. et al. Affinity fine-tuning of Boltz-2: an open framework for protein-ligand potency prediction in drug discovery. bioRxiv preprint (2026).
- Insilico Medicine announces preclinical drug discovery benchmarks from 2021 to 2024. News-Medical (2025).
- UKRI. Exscientia: a clinical pipeline for AI-designed drug candidates (case study).