Last year I mentioned reports that the startup incubator Ycombinator was thinking of getting into the biopharma field. Here’s a look at the current crop of potential companies.
One thing that stands out is that most of these seem to be focused on patient care or some sort of diagnostic. One exception is 20n, which is looking to engineer microbes to produce known pharmaceuticals or intermediates. That’s not at all a crazy idea, but the example given on the site (acetaminophen) is not a particularly compelling example by itself, since it’s extremely easy to make, from cheap precursors, on an industrial scale. And I’m not sure what to make of that “map of every chemical that can be made biologically”. It’s a nice graphic for the middle of the page, but there’s no telling what it means. I like a lot of the ideas kicking around in the synthetic biology field, but I can’t really say what 20n is up to yet.
The other one on the list I noticed is Atomwise, and I’ll let them speak for themselves:
Medicines are getting more expensive to develop. These days, it takes about $1.8 billion and 15 years for a single new drug. Atomwise aims to change that by using supercomputers to predict, in advance, which potential medicines will work, and which won’t. Our tools can tell the difference between great drug candidates and toxic ones, and discover new uses for old medicines.
Actually, what will speak for themselves are the results. If Atomwise can do this, at all, even poorly, then there are billions of dollars waiting out there for them to scoop up. But just the act of saying that you can do things like this makes me suspicious that they really can’t do things like this. Here’s a bit more:
Previous attempts haven’t always met expectations. The techniques of the day were limited by the knowledge and computers available. Today, things are different. We have invented cutting-edge machine learning algorithms that are built specifically for the world’s most powerful computers. We use one of the world’s top supercomputers to analyze databases 1000 times larger than those used in the past. This lets us deliver what many others can’t: precise and reliable medicinal predictions.
I could go on about this for a while, but in the end, these arguments are settled by data. Come on down and try it, guys. There’s plenty of room, and plenty to work on, so let’s see what you can get done. I’ll be watching with interest, and so will others.
Update, from the comments: “Hello Everyone, I’m the CEO of Atomwise and a long-time semi-lurker here. (I’m the anonymous who keeps asking what it would take to convince people that in silico methods work.) I agree that the proof will come from data; that’s one reason why we’re doing a large-scale evaluation with Merck. Send me an email (email@example.com) and I’d be happy to present our data from previous prospective validation projects to you. Or, if you have some minimally proprietary data against which you’d like to evaluate our predictive capability, let’s run the experiment. Best, Abraham Heifets”