| Google Scholar | https://scholar.google.com/citations?user=yfARKJgAAAAJ&hl=en |
| Google Scholar | https://scholar.google.com/citations?user=yfARKJgAAAAJ&hl=en |
2) What other possible applications could there be, and when would they be most useful in the development of antibody-based therapeutics.
One that jumps out to me is a similar display/sequencing-based A vs. B (or vs. C/D/E) screening of a target vs. anti-target(s). It seems another recent paper they cite, moves more in that direction.
https://www.sciencedirect.com/science/article/pii/S2667237522001278
In addition to uploading code, the authors also posted a web-based interface where you can input your own nanobody sequences.
http://18.224.60.30:3000/
What I'm really interested in going forward is:
1) How much data (sequencing-based or otherwise) would you need to get this degree of performance from a logistic model? They used ~60,000 sequences for "high" and "low" PSR binding (before some filtering), and later find if you increase it to 1M for each, the neural networks do a little better.