Investors and companies in the life science industry have been betting a lot of money over the last few years on a single idea: that computation will help us get a lot better at developing new drugs. But the word “computation” covers a pretty broad range of techniques. And the reason that there are dozens if not hundreds of computational drug discovery startups popping up is that everyone has their own hypothesis about what specific kind of computation is going to be the most powerful.

For example, you might be convinced that the most important thing is to understand the physics of protein-protein interactions, at an atomic level. And so you would put your money into atomic-scale simulations that show how proteins fold or unfold to form different shapes under different conditions. Or you might think that it’s more important to model proteins at the molecular scale, to make predictions about whether and how a particular drug molecule might dock with a target protein. Or you might think that it’s smarter to try to model whole cells and see how different molecular pathways interact to affect different functions of the cell. Or you might not care about the details of physics- or chemistry-based models at all. In that case could just take a big generative AI model, similar to a large language model, and train it on huge amounts of unlabeled data about genes and proteins in diseases cells and healthy cells to see what kinds of predictions it comes up with.

It’s too early to say which of these computational approaches—and which level or scale of focus—is going to be the most fruitful. But maybe you don’t have to choose. Maybe you can bet on all of these different ideas, all at once. Harry's guests this week are the CEO and CSO of a startup that’s taking an all-of-the-above approach. It’s called Deep Origin, and it was formed last year from the merger of two companies founded by theoretical chemist Garegin Papoian and software builder Michael Antonov. 

Antonov helped to found the virtual reality hardware company Oculus. After Facebook acquired Oculus, he got curious about longevity and how software could help untangle the trillions of gene-protein interactions that mediate health and disease. He founded a company called Formic Labs to dig into that problem, and last year the company changed its name to Deep Origin. Papoian, meanwhile, is a former academic scientist who’s who also took the helm as CEO of his startup AI and who’s interested in how to use software to model molecular dynamics and quantum chemistry. 

Recently Antonov and Papoian decided to join forces, and Biosim AI merged into Deep Origin. They say the company’s philosophy is that physics-based modeling by itself won’t be enough to build a powerful drug discovery engine. But neither will generative AI, which requires more training data than lab scientists will ever be able to provide. They think the only reasonable approach today is to combine the two, and use both physics and AI to try to get better at predicting which molecules could become effective drugs.

Exactly how Antonov and Papoian came to their conclusion, and how that integration is playing out, was the main theme of this week's conversation. It’s important stuff, because if Deep Origin is right, then a lot of other more specialized biotech and techbio startups could be going down the wrong path. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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