Sifting through the trillions of molecules out there that might have powerful medicinal effects is a daunting task, but the solution biotech has found is to work smarter, not harder. Genesis Therapeutics has a new simulation approach and cross-disciplinary team that has clearly made an impression: the company just raised a $52 million A round.
Genesis competed in the Startup Battlefield at Disrupt last year, impressing judges with its potential, and obviously others saw it as well — in particular Rock Springs Capital, which led the round.
Over the last few years many companies have been formed in the drug discovery space, powered by increased computing and simulation power that lets them determine the potential of molecules in treating certain diseases. At least that’s the theory. The reality is a bit messier, and while these companies can narrow the search, they can’t just say “here, a cure for Parkinson’s.”
Founder Evan Feinberg got into the field when an illness he inherited made traditional lab work, as an intern at a big pharma company, difficult for him. The computational side of the field, however, was more accessible and ended up absorbing him entirely.
He had dabbled in the area before and arrived at what he feels is a breakthrough in how molecules are represented digitally. Machine learning has, of course, accelerated work in many fields, biochemistry among them, but he felt that the potential of the technology had not been tapped.
“I think initially the attempts were to kind of cut and paste deep learning techniques, and represent molecules a lot like images, and classify them — like you’d say, this is a cat picture or this is not a cat picture,” he explained in an interview. “We represent the molecules more naturally: as graphs. A set of nodes or vertices, those are atoms, and things that connect them, those are bonds. But we’re representing them not just as bond or no bond, but with multiple contact types between atoms, spatial distances, more complex features.”
The resulting representation is richer and more complex, a more complete picture of a molecule than you’d get from its chemical formula or a stick diagram showing the different structures and bonds. Because in the world of biochemistry, nothing is as simple as a diagram. Every molecule exists as a complicated, shifting 3D shape or conformation where important aspects like the distance between two carbon formations or bonding sites is subject to many factors. Genesis attempts to model as many of those factors as it can.
“Step one is the representation,” he said, “but the logical next step is, how does one leverage that representation to learn a function that takes an input and outputs a number, like binding affinity or solubility, or a vector that predicts multiple properties at once?”
That’s the work they’ve focused on as a company — not just creating a better model molecule, but being able to put a theoretical molecule into simulation and say, it will do this, it won’t do this, it has this quality but not that one.
Some of this work may be done in partnerships, such as the one Genesis has struck up with Genentech, but the teams could very well find drug candidates independent of those, and for that reason the company is also establishing an internal development process.
The $52M infusion ought to do a lot to push that forward, Feinberg wrote in an email:
“These funds allow us to execute on a number of critical objectives, most importantly further pioneering AI technologies for drug development and advancing our therapeutics pipeline. We will be hiring more top notch AI researchers, software engineers, medicinal chemists and biotech talent, as well as building our own research labs.”
Other companies are doing simulations as well and barking up the same tree, but Feinberg says Genesis has at least two legs up on them, despite the competition raising hundreds of millions and existing for years.
“We’re the only company in the space that’s working at the intersection of modern deep neural network approaches and biophysical simulation — conformational change of ligands and proteins,” he said. “And we’re bringing this super technical platform to experts who have taken FDA-approved drugs to market. We’ve seen tremendous value creation just from that — the chemists inform the AI too.”
The recent breakthrough of AlphaFold, which is performing the complex task of simulation protein folding far faster than any previous system, is as exciting to Feinberg as to everyone else in the field.
“As scientists, we are incredibly excited by recent progress in protein structure prediction. It is an important basic science advance that will ultimately have important downstream benefits to the development of novel therapeutics,” he wrote. “Since our Dynamic PotentialNet technology is unique in how it leverages 3D structural information of proteins, computational protein folding — similar to recent progress in cryo-EM — is a nice complementary tailwind for the Genesis AI Platform. We applaud all efforts to make protein structure more accessible such that therapeutics can be more easily developed for patients of all conditions.”
Also participating in the funding round were T. Rowe Price Associates, Andreessen Horowitz (who led the seed round), Menlo Ventures, and Radical Ventures.