Drug discovery is broken. It takes over ten years and billions of kroner to develop a new treatment—and 90% still fail. Why? Because buried in the noise of biological data are weak signals that traditional tools can’t detect. We’re gambling on guesses—slow, expensive, and mostly wrong. Meanwhile, patients die waiting for drugs that never arrive. We’ve built an explainable AI platform that finds what others miss. Our neural network integrates multi-OMIC data and maps it against biological pathways with scientific clarity, not black-box predictions. We fuse this with language models primed with millions of biomedical papers. That means we don’t just find new drug targets, but repurpose existing drugs. We collapse drug discovery cycles from years to months, and from billions to millions.The AI-driven drug discovery market is exploding—$12B across Europe and the U.S., part of a 27B TAM. We're not selling software. We're selling time—and time is what every biotech and pharma company is desperate to save. Our approach cuts failure rates, slashes R&D burn, and gets viable candidates to trial faster. This isn’t just about our startup. It’s about turning Medicon Valley into Europe's answer to Kendall Square. More patents, more startups, more jobs. Denmark wins.

Høje mængder enzym i nogle afgrøder, sætter en begrænsning på afvendelsen af disse til fødevarer, da kvaliteten på disse ikke lever op til kundernes ønsker. I PhD projektet ”nanobody-baserede inhibitor” vil der blive udviklet både høj-specifikke og kryds-reaktive enzym inhiberende proteiner, der vil være skræddersyet for flere industrielle anvendelser. Vi forventer således at udvikle både first-in-class / best-in-class produkter, og samtidig introducere nye metoder i produktudviklingen.

OA er den mest udbredte gigtsygdom og rammer over 500 millioner mennesker verden over. Sygdommen er kompleks, drevet af forskellige molekylære mekanismer, hvilket fører til flere patientfænotyper. Der er ingen godkendte sygdomsmodificerende lægemidler (DMOADs) mod OA. Dette projekt bruger en biomarkørbaseret tilgang til at fremme DMOAD-udviklingen. Ved at anvende smertebiomarkører og patientrapporterede resultater (PROMs) identificeres smertefænotyper, mens NGF-veje udforskes for bedre behand.