The cynical part of me wonders: if this has been a promising approach for 10+ years, why weren't they able to secure VC funding years ago (or nonprofit biomedical research funding from places like the Gates Foundation that care a lot about infectious disease)?
I am totally onboard with the premise (as a TechBio-adjacent person), and some of the approaches you're taking (focused domain-specific models like Orthrus, rather than massive foundation models like Evo2).
I'm curious about what your strategy is for data collection to fuel improved algorithmic design. Are you building out experimental capacity to generate datasets in house, or is that largely farmed out to partners?
There are biotech companies like Eikon Therapeutics (https://www.eikontx.com/ ) where super-resolution microscopy in living cells is a central part of the platform.
There is also one widespread approach that isn't mentioned in the article: expansion microscopy. Expansion takes the scifi-sounding approach of: what if you could make your sample physically bigger? See the Wikipedia page for more: https://en.wikipedia.org/wiki/Expansion_microscopy
DeepMind/Google does a lot more than the other places that most HN readers would think about first (Amazon, Meta, etc). But there is a lot of excellent work with equal ambition and scale happening in pharma and biotech, that is less visible to the average HN reader. There is also excellent work happening in academic science as well (frequently as a collaboration with industry for compute). NVIDIA partners with whoever they can to get you committed to their tech stack.
For instance, Evo2 by the Arc Institute is a DNA Foundation Model that can do some really remarkable things to understand/interpret/design DNA sequences, and there are now multiple open weight models for working with biomolecules at a structural level that are equivalent to AlphaFold 3.
It is possible that they are licensing technology that was developed in academic science and are raising money to scale it up and get it ultra-standarized for commercial scale.
I agree that the modern Silicon Valley model of VC funding has been spoiled by SaaS startups, where the capital expense is smaller, the timeline to exit is shorter, and pivots are easier. It is not great for deeptech innovation because those require more capital, time, and are more technology-constrained than software. Ironically, modern VC was developed to support semiconductor startups (1970s-90s), but has drifted from that technology-heavy origin.
Indeed, now is the moment to step on the gas in biotech. The past 15 years have been nothing short of extraordinary in the field. We finally have the tools needed to effectively measure biology, manipulate biology, and increasingly predict biology. More recently, we have been able to turn more and more problems into computational problems.
With all of this coming together, we should be accelerating both public and private investment in biotechnology because we're getting closer and closer to transformative therapies. But...we're failing to rise to the occasion and meet the moment.
It is worth mentioning that China is heavily investing in biotechnology and they are getting genuinely good at the more commodified parts of the industry. This blog post [1] is long and aimed at a biotech expert audience, but one summary line that stands out is that "the drug industry is having its own DeepSeek Moment" [2].
To that end, I believe that this is the time to invest in the US biotechnology ecosystem so that we remain competitive with China. The ongoing crisis at the NIH is antithetical to this goal, as Derek Lowe's blog posts describe.
1) Creative projects beget creative projects. When you start working on one project, you'll have ideas for a dozen more, and probably one of those might actually be a good project idea to continue exploring and refining and shaping into a reasonable problem. It's really hard to come up with things if you're just staring at a blank sheet of paper, but working on anything at all gets this virtuous cycle started.
2) Talk to people! Bounce your ideas/areas of excitement off of other people, and see what gets reflected back at you. That signal can be very helpful to see when you've stumbled across an idea or problem that might be useful to more people than just yourself, i.e. a more important area of investigation.
3) Read, read, read. And take notes on random ideas you have while reading, and things that papers missed or didn't look into. If you do this enough and take some time to reflect on it, you can start to find gaps in knowledge that could be addressed.
I am pretty sure that the CGP Grey video was the core inspiration for this article. Nearly every image in the article was featured in the YouTube video, which predates the article by ~4 years.
Have you looked into Nebula Genomics? They will do 30X coverage WGS for $299, and will give you the underlying data so you can analyze it on your own (in addition to their analyses).
I would recommend the edX Introduction to Biology course [0].
It is a simplified version of the introductory biology course at MIT, that doesn't _focus_ on naming/defining things in biology. Instead, it uses the lenses of genetics and biochemistry to explore how the core machinery of life works, and how we got to our current understanding. That said, there is some amount of memorization that is unavoidable.
You either love it or hate it, depending on how well your electromagnetics class was taught.