10/🔗 To learn more, check out our paper and blog post! Our code is also fully open-sourced:

📄 Paper: arxiv.org/abs/2409.10588
🌐 Blog: olakalisz.github.io/adios-blog
🤖 Code: github.com/olakalisz/ad...

Excited to see where this research leads! 🚀

9/🙏 Huge thanks to my amazing coauthors who made this work possible!

Sebastian Towers, Philippe A. Roberts, Alicia Higueruelo, Francesca Vianello, Ming-Han Chloe Tsai, Harrison Steel, @jfoerst.bsky.social

This work was done at FLAIR @flair-ox.bsky.social , @ox.ac.uk

8/🧬 This is just the beginning...

ADIOS uses simplified models, but the core insight is huge: we can design therapies that remain effective AND guide evolution itself.

Next: cancer, antimicrobial resistance, any domain where we're fighting adaptive biological opponents! 🦠

7/🤯 But wait, there's more: shapers don't just defend better - they actively shape viral evolution 🧬

Viruses that evolved under pressure from H=100 shapers become easier for ALL antibodies to target - dark colours in the right-most column.

Attack is the best defense! ⚔️

6/🚀 And longer horizons work better...

As we increase the shaping horizon (H), antibodies get better at preventing long-term viral escape. H=100 shapers consistently outperform shorter horizons.

The meta-learning approach pays off - we're generating long-lasting therapies! 💊

5/📈 So do shapers actually work? Yes! ✅

We tested on dengue virus. Myopic antibodies start strong but lose effectiveness as the virus evolves - viral fitness goes up!

Shapers start slightly worse but maintain performance much better over time. They're playing the long game 🎯

4/⚡️ But there was a problem: this approach is computationally expensive

We needed fast binding calculations for hundreds of thousands of antibody-virus interactions.

💻 Solution: Reimplement the core simulator in JAX with GPU acceleration.
🚀 Result: 10,000x speedup

3/🧠 So how does ADIOS work? Through opponent shaping and meta-learning with 2 loops:

🔄 Inner loop: simulate how the virus evolves in response to each candidate antibody
🔄 Outer loop: optimise antibodies based on their performance across entire viral evolutionary trajectories

2/📉 Here's what happens with myopic therapies
✅ Therapy works against Virus A
❌ Selective pressure pushes evolution toward resistant Virus B
🔄 Back to square one

ADIOS flips this: instead of Virus B being resistant, we steer evolution toward easily-targetable Virus C

1/💡 Here's the key insight: viruses are stuck being myopic players

They evolve through trial-and-error mutations, reacting to selective pressures, including those our therapies create

But WE can (and SHOULD!) think ahead and anticipate how our therapies shape viral evolution