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Targeting Transcription Factors with AI, featuring Will Fondrie from Talus Bio
Manage episode 477395417 series 3526491
How do you drug the undruggable? In this episode of Data in Biotech, Ross Katz sits down with Will Fondrie, Head of Data Science and Engineering at Talus Bio, to explore how machine learning, mass spectrometry, and innovative computational models are transforming drug discovery. Learn how Talus Bio is targeting transcription factors—once considered out of reach—with scalable, high-impact data science.
What You'll Learn in This Episode
- Why transcription factors are historically hard to drug and how Talus Bio is changing that
- How mass spectrometry offers high-throughput, unbiased views of protein-DNA interactions
- The role of recommender systems in prioritizing compound testing
- Strategies for balancing build vs. buy in data infrastructure at scale
- Why open-source software is essential for scientific transparency and progress
Links:
- Find out more about Talus Bio: https://talus.bio
- Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
Connect with Will Fondrie on LinkedIn: https://www.linkedin.com/in/wfondrie/
Meet Our Guest
Will Fondrie is the Head of Data Science and Engineering at Talus Bio, a biotech company pioneering the development of small molecule drugs targeting transcription factors. With a PhD in molecular medicine and a background in proteomics, Will brings deep expertise in computational biology, machine learning, and scalable data systems.
About the Host
Ross Katz is the Principal and Data Science Lead at CorrDyn. He hosts Data in Biotech to spotlight innovative thinkers and data-driven leaders pushing the boundaries of biotechnology.
Enjoying the show? If you liked this episode, consider sharing it with a colleague and exploring more conversations at Data in Biotech. Your support helps us keep delivering expert insights on the future of biotech.
Sponsored by CorrDyn
This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.
46 episodes
Manage episode 477395417 series 3526491
How do you drug the undruggable? In this episode of Data in Biotech, Ross Katz sits down with Will Fondrie, Head of Data Science and Engineering at Talus Bio, to explore how machine learning, mass spectrometry, and innovative computational models are transforming drug discovery. Learn how Talus Bio is targeting transcription factors—once considered out of reach—with scalable, high-impact data science.
What You'll Learn in This Episode
- Why transcription factors are historically hard to drug and how Talus Bio is changing that
- How mass spectrometry offers high-throughput, unbiased views of protein-DNA interactions
- The role of recommender systems in prioritizing compound testing
- Strategies for balancing build vs. buy in data infrastructure at scale
- Why open-source software is essential for scientific transparency and progress
Links:
- Find out more about Talus Bio: https://talus.bio
- Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
Connect with Will Fondrie on LinkedIn: https://www.linkedin.com/in/wfondrie/
Meet Our Guest
Will Fondrie is the Head of Data Science and Engineering at Talus Bio, a biotech company pioneering the development of small molecule drugs targeting transcription factors. With a PhD in molecular medicine and a background in proteomics, Will brings deep expertise in computational biology, machine learning, and scalable data systems.
About the Host
Ross Katz is the Principal and Data Science Lead at CorrDyn. He hosts Data in Biotech to spotlight innovative thinkers and data-driven leaders pushing the boundaries of biotechnology.
Enjoying the show? If you liked this episode, consider sharing it with a colleague and exploring more conversations at Data in Biotech. Your support helps us keep delivering expert insights on the future of biotech.
Sponsored by CorrDyn
This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.
46 episodes
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