The Multi Layer Swiss Cheese Model of Optimization

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I wanted to learn about this like multi layer Swiss cheese model of optimization, you know, past just affinity. Are these optimizations happening in parallel or is it more sequential? Does that make sense? So I'd love to know more about more. And so earlier, Sean, too, you mentioned this concept of humanization when you were talking about, I think, a transgenic mouse model. But what we showed with this model is that it's inversely correlated to immunogenicity and has pretty good correlation to developability and manufacturer ability.

Drug development is an extremely expensive endeavor, in large part because of the 96% failure rate of clinical drug trials. However, the new frontier of medicine has arrived, and Sean McClain and Joshua Meier (respectively the Founder/CEO and Chief Artificial Intelligence (AI) Officer of Absci) are leading the charge. Absci is a public company harnessing generative AI to create more effective medicines faster and less expensively. During this episode, Sean and Joshua explain how their pooled approach to antibody production works, why they focus so heavily on data, the metrics they use to evaluate their models, and why they are certain that personalized medicine is going to be a reality in the future. There are countless possible mashups of AI and drug discovery, and Absci attempts to bring an innovative approach to biologics to positively impact human health!

“We’re not just developing AI within biologic drug discovery for the sake of it. We’re really wanting to utilize this to be able to discover new biology.” — @SeanRMcClain Key Points From This Episode:

Sean’s original idea behind the founding of Absci and how it has evolved into what it is today.
How Absci is making the process of drug creation much more efficient.
The career path that led Josh to Absci.
How antibodies are currently produced.
The most important aspects to consider when developing an antibody.
A high-level overview of how Absci generates data.
The number of AI-generated designs that Absci can validate in any given week.
Metrics that Absci uses to evaluate their models.
What humanization is in the context of antibody design.
Understanding Absci’s naturalness model.
A common problem in the AI field.
The potential for collaboration with Big Pharma.
How the models Absci is developing can be applied in different contexts.
Sean and Joshua’s thoughts on what the future of medicine is going to look like.
How AI is likely to change the way we approach scientific and technological developments.