Wet-lab innovations will lead the AI revolution in biology

> people unacquainted with biology have a false perception of how low-throughput biology experimentation is. In many ways, it can be. But the underlying physics of microbiology lends itself very well to experiments that could allow one to collect tens-of-thousands, if not millions, of measurements in a singular experiment. It just needs to be cleverly set up.

I think this passage gets to the fundamental rift of disagreement in perspective between those focused purely on computational advances versus innovating in wet lab techniques.

Why? Because years of peoples' careers have been wasted waiting on promises from molecular biologists claiming they will make these "clever" high-throughput experiments work. In my experience, they'll spend months to years concocting a Rube Goldberg machine of chained molecular biology steps, each of which has (at best) a 90% success rate. You don't have to chain many of these together before your "clever" setup has a ~0% probability of successfully gathering data.

In biology, the most important step is finding the right thing to measure. Biological systems are highly contextual, so the second most important step is finding the second thing to measure in relationship to the first thing.

In the case of AlphaFold, measuring crystal structures is the most important thing (molecular phenotype). The second most important thing is measuring many genomes. Multiple sequence alignments allows evolution (variation under selection) to tell you about the important bits of the structure. The distance from aligned DNA sequences to protein structure isn't a bridge too far.

Unfortunately, biology has been mislead by the popularity of transcriptomics, which the post touches on briefly (limits of single-cell approaches). Transcriptomics generates lots of data (relatively) cheaply, but isn't really the right thing to measure most of the time because it is too far removed causally from the organismal phenotype, the thing we generally care about in biomedicine. Although gene expression has provided some insights, we've exhausted most of its value by now and I doubt ML will rescue it (speaking from personal experience).

The flip side of this is that progress in ML for biology is always going to be _slower_ than progress in ML for natural languages and images [1].

Humans are natural machines capable of sensing and verifying the correctness of a piece of text or an image in milliseconds. So if you have a model that generates text or images, it’s trivial to see if they’re any good. Whereas for biology, the time to validate a model’s output is measured more in weeks. If you generate a new backbone with RFDiffusion, and then generate some protein sequences with LigandMPNN, and then want to see if they fold correctly … that takes a week. Every time. Use ML to solve _that_ problem and you’ll be rich.

TFA mentions the difficulty of performing biological assays at scale, and there are numerous other challenges. Such as the number of different kinds of assays required to get the multimodal data needed to train the latest models like ESM-3 (which is multimodal, in this context meaning primary sequence, secondary structure, tertiary structure, as well as several other tracks). You can’t just scale a fluorescent product plate reader assay to get the data you need. We need sequencing tech, functional assays, protein-protein interaction assays, X-ray crystallography, and a dozen others, all at scale.

What I’d love to see companies like A-Alpha and Gordian and others do is see if they can use the ML to improve the wet lab tech. Make the assays better, faster, cheaper with ML. Like how they use ML to translate the electrical signals of DNA passing through the pore into a sequence in the Nanopore sequencers. So many companies have these sweet assays that are very good. In my opinion, if we want transformative progress in biology, we should spend less time fitting the same data with different models, and spend more time improving and scaling wet lab assays using ML. Can we use ML to make the assay better, make our processes better, to improve the amount and quality of data we generate? The thesis of TFA (and experience) suggests that using the data will be the easy part

1. https://alexcarlin.bearblog.dev/why-is-progress-slow-in-gene...

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