📢 Our new #manuscript shows how zero-shot Generative AI can create de novo antibodies from scratch.
🏅Hundreds of antibodies are created zero-shot and validated in the wet-lab for the first time ever.
Paper: biorxiv.org/content/10.110…
Data: github.com/AbSciBio/unloc…
🏅Hundreds of antibodies are created zero-shot and validated in the wet-lab for the first time ever.
Paper: biorxiv.org/content/10.110…
Data: github.com/AbSciBio/unloc…
Our zero-shot generative AI framework. The model is programmed with a target antigen structure and a chosen antibody scaffold sequence.
Antibodies are then generated de novo.
Note: all proteins binding the target (or its homologs) were removed from the training set.
Antibodies are then generated de novo.
Note: all proteins binding the target (or its homologs) were removed from the training set.
We validate generated antibodies experimentally in the wet-lab.
Our platform is extremely high throughput. We can test ~3 million unique AI-generated designs each week.
A cycle of [AI ➡️ lab ➡️ data] takes just 6 weeks.
Our platform is extremely high throughput. We can test ~3 million unique AI-generated designs each week.
A cycle of [AI ➡️ lab ➡️ data] takes just 6 weeks.
Example AI-generated cancer drug leads. Our model generates antibodies with stronger affinities than a highly optimized therapeutic drug.
The binders come straight out of the model.
In some cases, >90% of the CDR3 region has changed.
The binders come straight out of the model.
In some cases, >90% of the CDR3 region has changed.
Generative models have a tendency to memorize the training set. However, our model generates HCDR3s that are very different than those observed during training (on average, 5/13 positions are changed).
The sequences also differ from known antibodies in massive databases.
The sequences also differ from known antibodies in massive databases.
We predict structures of the discovered antibodies, finding that their conformations are highly flexible.
However, the model still learns to place key residues in appropriate positions, likely mediating binding against the epitope.
However, the model still learns to place key residues in appropriate positions, likely mediating binding against the epitope.
We're confident in our experimental platform, which can now screen millions of unique AI-generated designs in a few weeks.
To that end, we've opened the data behind this manuscript, releasing the full list of sequences and their measured affinities.
Enjoy! 🤓
To that end, we've opened the data behind this manuscript, releasing the full list of sequences and their measured affinities.
Enjoy! 🤓
We're just getting started. De novo antibody design will unlock a new generation of better therapeutics, with higher probability of success.
We are leveraging this tech to get antibodies into the clinic in just 18-24 months, down from six years.
We are leveraging this tech to get antibodies into the clinic in just 18-24 months, down from six years.
Special thanks to the dream team for pulling this off 🌟
Not to mention the 200+ unlimiters working to make this a reality.
We have a dozen open roles for AI scientists. Come join us in transforming drug creation w/ Generative AI 🚀
Not to mention the 200+ unlimiters working to make this a reality.
We have a dozen open roles for AI scientists. Come join us in transforming drug creation w/ Generative AI 🚀
Great coverage this morning on our advancements from @ForbesTech & @VentureBeat:
forbes.com/sites/johncumb…
forbes.com/sites/johncumb…
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