Quris is a dual‑headquartered (Boston and Israel) AI company operating a Bio‑AI Clinical Prediction Platform to better predict which drug candidates will safely work in humans. It uses a patented Chip‑on‑Chip technology to test thousands of known and novel drug candidates on hundreds of genetically diverse, miniaturized patients‑on‑a‑chip. Its automated, self‑training BioAI platform generates proprietary data and applies machine learning to quickly predict clinical safety and efficacy, with applications in new drug development, repurposing, and personalization. Quris positions this approach to avoid the tremendous costs of failed clinical trials and accelerate selection of candidates for clinical testing. The company plans to scale to rapidly scan hundreds of drug candidates to further train its AI engine, expand its team, and build partnerships as it prepares its lead discovery for clinical testing. Quris is backed by strategic biotech and big‑data investors and is led by a team of AI and medical research specialists. Quris combines linked organ‑on‑a‑chip organoid systems with machine learning to simulate human physiology at scale, enabling faster, cheaper preclinical testing without mice. Its "chip‑on‑chip" serial organoid approach uses less biological material and automation to run many simulated humans at a fraction of the cost of academic setups. The company trains AI models on experimental data to interpret multi‑organ signals and prioritize drug candidates, aiming to replace large animal cohorts with dozens-to-hundreds of automated tests. Quris has an agreement with the NY Stem Cell Foundation for stem cell workflow access and lists Robert Langer and Nobel laureate Aaron Ciechanover on its advisory roster. It pursues two business lines: a results‑contingent screening service for pharma and internal drug development; the company is advancing a Fragile X treatment toward clinical testing next year. CEO Isaac Bentwich says the technology supports more personalized medicine and that initial funding will productize the setup and train the AI on the first hundreds-to-thousands of drugs.

Total raised

$37M

Funding rounds

2

Latest round

Seed

Latest activity

Jan 2022