Better antibodies by engineering targets, not engineering antibodies (Nabla Bio)

Note: Thank you to Surge Biswas (founder of Nabla) for comments on this draft and and Dylan Reid (an investor into Nabla) for various antibody discussions!

Antibody design startups are singlehandedly the most common archetype of bio-ML startup out there. It’s understandable why — antibodies are derisked modalities, CDR loops driving antibody efficacy makes the whole structure more amenable to ML, and there’s a fair bit of pre-existing data there. But, because it’s also the most common form of company, it’s difficult to really differentiate one over the other.

If you squint, you could make out some vague distinguishing characteristics. Bighat Biosciences does a lot of multi-property optimization, Absci and Prescient have the strongest external research presence, and so on. But there is a vibe of uniformity. It’s nobody’s fault, that’s just the nature of any subfield that has a huge amount of money flowing into it; everyone quickly optimizes. And, unfortunately for those of us who enjoy some heterogeneity, most everyone arrives at the same local minima.

Because of that, I’ve never really wanted to write about any antibody company in particular. None of them felt like they had a sufficiently interesting story. All fine companies in their own right, but they all tell the same tale: great scientists, great high-throughput assays, great machine-learning, and so on.

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