Qnity
case study
Electrochemical, label-free carbohydrate detection
Pre-Seed
Brasil

What's the Problem?

Early drug discovery includes the steps to discover and/or define the correct biological target for a disease and compare it to the best candidate compounds. It is a risky, lengthy and expensive endeavor that absorbs around USD 60 million in investments and 4-5 years to achieve the right hopeful molecule that will be taken to clinical development. This match is the critical step: it shows that the candidate has the potential efficacy, and affinity is how researchers measure this union. The higher the affinity, the better the efficacy and therefore the more promising the candidate. However, measuring affinity is not an easy task. Traditionally, lab-based methods are cumbersome and slow-paced, but they offer accuracy. On the other hand, computer-based techniques can process thousands of compounds and simulate binding affinity, but at the cost of lower accuracy. More recently, artificial intelligence (AI) is being applied in these screening activities, but so far, no AI-created drugs have been approved on the market.

Qnity
Qnity
Qnity
Qnity

How are they Solving it?

At Qnity, we designed a chip based on quantum electrochemistry concepts to accurately calculate binding affinity with real compounds and targets. Dubbed QLab™, it brings the best of both worlds: the precision of traditional methods and the processing power of computational approaches. And this higher affinity data generated by QLab™ will be used to train Qnity's proprietary AI, which will aid faster selection and optimization of candidates to move into drug development, shortening screening steps and reducing costs. QLab™ sets Qnity apart as a hardware and software company.

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