The AI × AAV Interpreter

  TL;DR   ML in AAV engineering goes far beyond capsid design; 11 application areas, ranked by maturity.  Most conversations about ML in AAV focus on one thing: designing better capsids. But the field is broader than that — and it has been quietly expanding for years. ML is now touching manufacturing, vector genome quality, regulatory elements, receptor identification, and even automating parts of R&D workflows. If you've been tracking only the capsid engineering headlines, you've been seeing a fraction of what's actually happening. The application map in this post grew out of a list I started in 2018 to track where ML was showing up in the AAV field. What began as a handful of entries, mostly packaging prediction and early tropism work, has grown into 11 distinct application areas as the field matured, diversified, and moved from proof-of-concept to something closer to infrastructure. This post is that map, made accessible. The goal is practical: if you're an AAV ...

TL;DR  ML can help you along every step of the AAV campaign; you just need to know how it fits . You now know what generative and predictive models do from the previous posts of this series [ Post 1 , Post 2 ]. Generative proposes new sequences. Predictive scores the ones you have. But knowing what they do isn't the same as knowing when to use them. When do you bring ML into your campaign? At what step? What decisions does it actually change? This post maps ML onto your real workflow — so you know where it fits and where it doesn't. The Big Picture ML doesn't replace your workflow. It augments specific steps. You still design libraries. You still synthesize. You still screen. You still validate. ML inserts at two points: Before you make things: Generative models propose candidates. Predictive models filter out likely failures. You synthesize smarter. Before you test things: Predictive models rank your variants. You screen the most promising first. You prioritize smarter. Th...

   TL;DR  AI does not have enough data to replace you. You are the one making both data and AI.

TL;DR  AI doesn't have enough data to replace you. You are the one making the AI.    Every couple of days, someone slides into my inbox with a version of the same question. A postdoc. A VP of Research. A senior scientist with fifteen years in capsid biology watching presentations about models that "design novel AAV variants at scale." The question is always: " Should I be worried? " Not about what you think. The fear makes sense. The headlines are real. But the anxiety is aimed at the wrong target. There are three structural reasons why — and each one reframes the previous. Realization One: The Models Need You to Function at All Before asking whether AI will replace experimental scientists, ask whether it can even work reliably in AAV engineering. The answer is: barely — and not because the algorithms aren't good enough. Of the roughly 246 million protein sequences in UniProt, fewer than 600,000 have been manually reviewed and experimentally characterized. T...