cover of episode EP 111: Combining human and machine intelligence in protein engineering with Dr. James Field, CEO of LabGenius

EP 111: Combining human and machine intelligence in protein engineering with Dr. James Field, CEO of LabGenius

2023/11/9
logo of podcast The Genetics Podcast

The Genetics Podcast

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In this episode of The Genetics Podcast, we welcome Dr. James Field, founder and CEO of LabGenius. Join us as we delve into LabGenius' cutting-edge approach that utilises machine learning, artificial intelligence, and sophisticated robotics to advance antibody discovery and drug development. As a bonus, learn about James’ path from scientist to CEO, and how he created LabGenius.Timestamps:*00:00 Introduction*01:04 The genesis of LabGenius: protein engineering04:06 Progressing from human intuition and rules-of-thumb to a mathematically-driven approach to protein synthesis05:24 Redefining the relationship between human and machine08:38 Multi-dimensional optimisation: an iterative machine learning approach to design and test various molecules11:20 The LabGenius Process to developing antibodies: sequencing, testing mammalian expression, and functional cell-based assays12:50 The relative scalability of functional cell-based assays14:30 How LabGenius is tackling the bottlenecks in finding antibodies to cure cancer15:52 Applying protein engineering beyond antibodies to enzymes and other biological molecules18:12 The challenges in scaling clinical trials and the choice to engage with partners19:24 James’ shift from scientist to CEO: Overcoming the challenges of fundraising and finding smart investors who share your vision24:40 James’ entrepreneurial spirit: wanting to build something that is worth the tough journey26:10 The biggest challenges to creating LabGenius and lessons learned from starting a company29:00 Navigating the complexities of partnering with large pharma and biotech companies30:42 Novel technologies on the horizon that redefine what it means to be healthy**32:48 Future Frontiers: The potential for in-silico models in advancing drug discovery34:58 **Closing Remarks