We put together a list of the top YC companies by valuation as of October 2018. You can see that list at https://ycombinator.com/topcompanies.
Here’s a Q&A with Abraham Heifets, Cofounder of Atomwise, one of the companies featured on the list.
What does Atomwise make/do?
Atomwise helps biological researchers to discover new medicines. We invented the use of deep neural networks for structure-based drug design.
How many employees does Atomwise have?
How many founders?
Two; Izhar Wallach and I co-founded Atomwise while we were P.h.D. students studying computational biology at the University of Toronto.
What is your most impressive recent product milestone?
This year, we’re working on over 200 academic collaborations, with institutions like Dana Farber Cancer Institute, Tulane, and Duke University. We’ll deliver more than 5,000 compounds to researchers across the world. For context, the largest pharma companies in the world typically work on about 60 small molecule projects at a time.
What is the larger impact / societal impact of your product in the space you work within? We work on diseases ranging from childhood cancers, to Alzheimer’s, to antibiotic-resistant infectious disease. Most machine learning and software engineers never get to help cure a person, but here there’s a direct link between our day-to-day work and the benefit to people’s lives.
What’s an interesting element of Atomwise’s company culture? People have been trying to solve chemistry with computers for decades, so for us to succeed, we need to have the top experts across a number of domains. We’re fortunate to work alongside some of the brightest minds in machine learning, infrastructure engineering, medicinal chemistry, physics, and structural biology. We value the empathy, curiosity, clear thinking, and communication that it takes to fruitfully collaborate across disparate disciplines and diverse perspectives.
Looking back, what motivated you to start Atomwise? There’s numerous concurrent factors at play: new machine learning algorithms, massive datasets, ubiquitous computation, and an industry that needs new innovations. But more than anything else, we have a chance to help more people now than at any time in history.
Is what you’re working on now the original idea or did you pivot? We’ve expanded on the original idea. While we began with a narrow focus on machine learning approaches to structure-based small molecule binding affinity prediction, we’re now able to address all stages of pre-clinical drug discovery.
Were there moments where you thought the company might die? Describe one of those and anything you learned from it. I’d tried and failed to raise money for the entire year before we got into Y Combinator. At one point, we were two months away from bankruptcy. YC teaches how to run both Seed and Series A fundraises but, at the time, that kind of training wasn’t publicly available. Read the lessons on the YC blog!
What was a particularly important insight you had about your market that made your product work? The language and the evidence that resonates with computational chemists is different from what we need for biologists and chemists, which is different again from what resonates with business development people. Success is a team effort.
What’s one piece of advice you’d share with a young founder? When you’re reading advice, remember https://xkcd.com/1827/