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Digital Twins: The Future of Drug Discovery with Dr. Tomáš Helikar

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Manage episode 455808921 series 3593861
Content provided by BioTalk Unzipped. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by BioTalk Unzipped or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

BioTalk Unzipped, hosts Gregory Austin and Chad Briscoe engage with Dr. Tomas Helikar in an exciting discussion on the future of AI, digital twins, and personalized medicine. From mathematical models and immune system simulations to groundbreaking collaborations with the Department of Defense, this episode uncovers how digital twins have the potential to transform drug discovery and clinical trials. Don’t miss insights on the ethical challenges of data privacy, the promise of animal digital twins, and the latest AI breakthroughs like MIT’s Sparrow algorithm!


Chapters

01:00 How the mathematical models work. Normalization and Scaling in Computational Biology

07:55 Focus on the Immune System and Organ Models

09:05 Collaboration with the Department of Defense

11:03 Drug Discovery and Digital Twins

13:43 NEW NEWS! - AI in Drug Discovery: The MIT Sparrow Algorithm

17:51 Future of Digital Twins in Clinical Trials

18:59 Personalized Medicine and Individualized Treatments

23:33 The Potential of Animal Digital Twins

24:59 Building a Research Infrastructure for Digital Twins

26:03 Privacy and Ownership of Digital Twin Data

30:14 Encouraging Publication and Literature Engagement


How to reach us:

Tomas Helikar

https://www.linkedin.com/in/tomashelikar/

X - https://x.com/tomashelikar


Dr. Chad Briscoe

https://www.linkedin.com/in/chadbriscoe/

Celerion - https://www.celerion.com/


Gregory Austin

https://www.linkedin.com/in/gregoryaustin1/

ECI - https://eci-rx.com /


Sound Bites

"Publications are the currency in academia."


New News Story:


Image Credits


  continue reading

34 episodes

Artwork
iconShare
 
Manage episode 455808921 series 3593861
Content provided by BioTalk Unzipped. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by BioTalk Unzipped or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

BioTalk Unzipped, hosts Gregory Austin and Chad Briscoe engage with Dr. Tomas Helikar in an exciting discussion on the future of AI, digital twins, and personalized medicine. From mathematical models and immune system simulations to groundbreaking collaborations with the Department of Defense, this episode uncovers how digital twins have the potential to transform drug discovery and clinical trials. Don’t miss insights on the ethical challenges of data privacy, the promise of animal digital twins, and the latest AI breakthroughs like MIT’s Sparrow algorithm!


Chapters

01:00 How the mathematical models work. Normalization and Scaling in Computational Biology

07:55 Focus on the Immune System and Organ Models

09:05 Collaboration with the Department of Defense

11:03 Drug Discovery and Digital Twins

13:43 NEW NEWS! - AI in Drug Discovery: The MIT Sparrow Algorithm

17:51 Future of Digital Twins in Clinical Trials

18:59 Personalized Medicine and Individualized Treatments

23:33 The Potential of Animal Digital Twins

24:59 Building a Research Infrastructure for Digital Twins

26:03 Privacy and Ownership of Digital Twin Data

30:14 Encouraging Publication and Literature Engagement


How to reach us:

Tomas Helikar

https://www.linkedin.com/in/tomashelikar/

X - https://x.com/tomashelikar


Dr. Chad Briscoe

https://www.linkedin.com/in/chadbriscoe/

Celerion - https://www.celerion.com/


Gregory Austin

https://www.linkedin.com/in/gregoryaustin1/

ECI - https://eci-rx.com /


Sound Bites

"Publications are the currency in academia."


New News Story:


Image Credits


  continue reading

34 episodes

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