We design and apply unique patient-based cellular assays for large-scale phenotypic screening, leaning on the power of AI-driven image analysis. Integrated from bed to bench and back to bed, we aim to generate most efficient first-in-class therapeutic molecules for next generation medicine.
Keep posted about
our latest developments
Ksilink is happy to announce the initiation of the ANR funded project "Target-agnostic drug discovery approaches for neuromuscular diseases (TREAD)". This project is supporting...
Ksilink is happy to have welcomed the team of Prof. Valérie Mezger-Lallemand from Université Didérot to initiate a collaboration with the aim to establish a new patient-based disease model to identify …
We are proud to be present with two posters during the AI powered Drug Discovery Symposium from 8-9th of November 2021 in London, at Francis Crick Institute. Like to schedule a...
They trust us
“Patient-derived disease models used for phenotypic drug discovery purposes carry a huge potential. We strongly believe in Ksilink as a key player for effectively bridging the gap between scientific excellence and the pharma industry.”
Jacques Volckmann, Head of R&D, Sanofi, France
“Ksilink’s capacities to apply patient-derived disease models for high-throughput, high-content screening by using AI-driven deep learning computation is outstanding.”
Wolfram Zimmermann, CSO, Myriamed GmbH, Germany
“Bridging the gap between academic clinical research and industry is key to enable the precision medicine of tomorrow. We see big value in the ability of Ksilink to translate clinical expertise into treatment for diseases with high unmet medical need .”
Andreas Meyer-Lindenberg, Director and CEO, Central Institute of Mental Health, Germany
“Phenotypic screening using most authentic disease models is clearly paving the way towards next generation medicine. We strongly believe in Ksilink’s potential, allowing the generation of valuable first-in-class therapeutic molecules.”
Bert Klebl, Managing Director of Lead Discovery Center, Germany