40: Computational Antibody Drug Discovery Platform with Machine Learning Startup, Antiverse, with the Co-Founder Murat Tunaboylu

June 26, 2018

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Machine Learning, Antibodies, and Drug discovery.

Antiverse is building a world-first computational antibody drug discovery platform. We combine in-house lab expertise with state of the art machine learning to predict antibody-antigen binding and provide antibody drug candidates in one day. Source

Click here for the quick text interview we had:

Quick Text Interview with Murat Co-Founder of Antiverse An AI Antibody Drug Discovery Platform

Website

Contact info: info@antiverse.io

“Antiverse: Leveraging AI to Achieve Antibody Drug Discovery in One Day

BETTER MEDICINES FASTER

Antiverse is building a world-first computational antibody drug discovery platform to predict antibody-antigen binding and provide antibody drug candidates in one day.

A combination of state of the art machine learning and cell-free protein synthesis is used to predict antibodies that bind to a given antigen target with high affinity. The resulting software can then take antigen taget sequence, provided by the customer, and do a high-throughput screening of all possibilities of antibody sequences to detect the sequence that will produce a high-affinity antibody for the target. The customer will be provided with the sequence in a single day, thus reducing the time typically required for antibody therapeutics discovery by 3 to 18 months.” Source

The Team (see more on their website)

Murat Tunaboylu has a BSc in Electrical Engineering and twelve years of experience as a software engineer. He also has extensive experience in labware automation and will be automating the lab work and creating the software that customers will use. Linked in Profile

Rowina has a PhD in Biochemistry from the University of Cambridge in cell-free protein synthesis and will be using this cutting-edge technique for the high-throughput generation of antibody fragments. She previously worked as a protein synthesis scientist for drug development for the contract research organisation Domainex. Linkedin Profile 

Ben Holland has an MEng in Engineering Science from the University of Oxford and experience in mathematical modelling, especially neural networks, and will use the generated data to train the machine learning system. Linkedin Profile 

Hyperlinked Timestamped Show Notes

  1. [ 01:00 ] What is unique about what they are developing, Murat’s background, and the amazing improvements they are working to create in decreasing drug discovery time
  2. [ 03:40 ] His origin story of getting into biotech, and what drives him to work long hours to create something from nothing.
  3. [ 05:35 ] The key skills that his co-founders bring to the team.
  4. [ 05:55 ] How the machine learning works, and how difficult it is to understand.
  5. [ 08:30 ] If the technology ever replaces humans and digitizes the entire process of drug development.
  6. [ 09:40 ] How long from today it will take them to have the finished product that they are developing.
  7. [ 12:00 ] Key people, skills, and resources they need to help develop Antiverse.
  8. [ 13:10 ] How certain he is in what they are trying to develop coming into the world.
  9. [ 14:05 ] What is unique about what they are developing that gives them a competitive edge.
  10. [ 16:50 ] What partnerships they’re looking for.
  11. [ 17:35 ] What made him think now is the best time to build Antiverse, and what key things helped him along the way.
  12. [ 20:10 ] We start a discussion about having three directors over having a CEO, COO, or CTO. Really great space ship analogy here.
  13. [ 23:35 ] Who the first patch of customers he expects to have will be.
  14. [ 24:25 ] What he is a nerd about, and if he ever goes missing, what we should think the reason is. Also, we talk about a unique way to raise funds based on a hobby of his.
  15. [ 26:06 ] With unlimited resources, what he and Antiverse would do.
  16. [ 26:55 ] Key people that inspire him. Also, we talk about Jurassic Goats.
  17. [ 27:55 ] Key mentors for him, and how they helped him/Antiverse.
  18. [ 28:45 ] The types of things mentors have helped him with, and what mentors he is looking for.
  19. [ 29:45 ] What key advice/suggestions he would give to others after reflecting on his journey.
  20. [ 31:00 ] Key ways to follow along with their journey.

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