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Webinar

AI and HCS in cancer research: case studies on faster target discovery and screening

This webinar provides a firsthand look at how bioinformatics and AI are transforming High Content Screening (HCS) and drug discovery with reliable, proven results. Ideal for scientists, researchers, and industry professionals, this session offers valuable insights into the practical impact of advanced HCS techniques in a real-world lab setting.

Webinar on demand

What You’ll Discover:

  • Case Study from the Lab: Ryvu Therapeutics presents their ONCO Prime platform, which utilizes patient-derived cells to develop cancer models that are clinically relevant. By integrating patient medical history with molecular data, ONCO Prime enables the discovery of new therapeutic targets, enhancing translational potential for effective treatments.
  • Ardigen’s AI-Enhanced HCS Solution: See how Ardigen’s AI-driven solution advances High Content Screening by predicting cell proliferation directly from DAPI images in  complex cellular cultures, streamlining workflows and lowering costs. These proven HCS results highlight how AI can optimize screening processes for advanced cellular cultures models in real lab environments.
  • Ensuring Data Quality in HCS: Ardigen’s quality control features detect technical artifacts that may not be visible to the human eye in cell painting experiments, ensuring that the data used in HCS is accurate and reliable for critical decision-making in drug development.

About the webinar

Join us for a webinar that brings real-world insights directly from the lab, showcasing how AI and bioinformatics are making a tangible impact on drug discovery. In this session, we’ll explore practical results from advanced phenotypic screening and data analysis developed through the collaboration between Ardigen and Ryvu Therapeutics.

 

 

 

 

 

 

 

Meet our speakers:

Andrzej Mazan, PhD
Associate Director at Ryvu Therapeutics

Dr. Andrzej Mazan is an Associate Director with over 15 years of experience in cancer research. He holds a Master's degree in biotechnology from Jagiellonian University and the University of Orleans, France, and earned his Ph.D. at the Manchester Cancer Research Centre, UK, where he studied transcription factors in blood cell formation and cancer development. Dr. Mazan contributed to drug discovery projects as part of a multidisciplinary team at Cancer Research Technology in Cambridge, UK, and helped develop methods for target identification and validation. Since 2016, he has worked on numerous projects at Ryvu, collaborating with both internal teams and external partners. Currently, he leads ONCO Prime, a project to identify novel molecular targets using primary-derived cell models (PDCs). Dr. Mazan is a member of the American Association for Cancer Research (AACR) and co-author of numerous scientific publications.

Krzysztof Rataj, PhD
Cheminformatics Data Scientist at Ardigen

Dr. Krzysztof Rataj is a specialist in drug discovery, working at the nexus of medicinal chemistry, biology, imaging, and artificial intelligence. He earned his Ph.D. in Biophysics from Jagiellonian University and has been part of various international projects in collaboration with teams from Hungary, Denmark, and Norway. His team is focused on using AI to improve drug design and precision medicine, tackling challenges like property prediction, compound optimization, and applying new methods to study proteins and nucleic acids. They're also working on using computer vision to analyze screening images and histopathology data. Their latest project aims to combine chemical structure data with phenotypic screening to enhance high-content screening processes.

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Meet our hosts:

Miika Ahdesmäki
CIO at Ryvu Therapeutics

Miika Ahdesmäki is CIO at Ryvu Therapeutics and has fifteen years of experience in large pharma and biotech. Miika has a PhD in Computer Science and an MBA from University of Cambridge. At Ryvu Miika is accountable for transforming and accelerating drug discovery as head of data science and IT. He is passionate about leveraging machine learning and AI in biomarker and drug discovery & development.

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