Recently we talked before how big data is the new frontier with just .05% of all data available today having been analyzed. There are really two reasons why this number is so low:
- 98% of big data has only been created in the last several years.
- A meaningful amount of this big data is the incessant drivel that you see people vomiting all over every form of social media (data which is turning out to be surprisingly useful in predicting whether or not they’ll pay back a loan).
This means that all kinds of gold prospectors are lining up with their freshly crafted artificial intelligence (AI) algorithms looking to extract all the value they can from this wild west of data before someone else does. Perhaps nowhere is there more excitement at the moment than the applications to be had in the healthcare industry. Here’s a look at just some of the startups that are applying artificial intelligence and big data to healthcare (courtesy of the bright minds over at CB Insights):
The application that we’ve circled above is “drug discovery” using AI or what’s also known as “computational drug discovery”. The reason that this is now a thing is not just because of all the big data that’s available now, but also because of how cheap cloud computing has become, not to mention the emergence of deep learning algorithms. Earlier last year we threw together a list of 4 startups playing in this space, and later came across another called BenevolentAI that made our list of the 5 biggest AI startups. Now we want to take an updated look at the players in this list and focus on what makes them different.
In order to help us do that, Andrew Radin, co-founder of TwoXAR, kindly offered his assistance. If you recall from our last article, Andrew M. Radin was one of the two “Andrew Radins” who founded TwoXAR which explains the startup’s name. Yes, the two actually met while quarreling over www.andrewradin.com which is maybe the coolest “how you got started” explanation ever. Andrew M. helped explain the drug discovery process in the simplest of ways as follows:
- Find a new protein in body to hit with molecule
- Find molecule(s) that binds to protein in body
- Once you find a hit, then turn into something that can be introduced to a living being
When it comes to computational drug discovery, a startup can focus on one or many of these steps. Let’s take a closer look at 9 startups involved in computational drug discovery.
The most valuable of the bunch is British unicorn BenevolentAI which has taken in $100 million in funding at a valuation ($1.78 billion) that makes them the largest AI startup in Europe and one of the top 5 biggest in the world. With a life science paper being published every 30 seconds and an FDA approval process that is all but broken, BenevolentAI plans to use AI to speed up the drug discovery process such that they plan to “sell their own drugs directly in the next 4 years“. An article published yesterday by Business Insider says that BenevolentAI “signed an $800 million deal in 2014 to hand over two Alzheimer drug targets to an unnamed US company for development” and that since they started in 2014, they now have “24 drug candidates“. So yeah, seems like pretty promising stuff. Nerds like us will enjoy the unique way in which they chose the name for their first clinical study.
Founded in 2007, San Bruno California startup Numerate has taken in over $40 million in funding from investors that included Eli Lily to develop a computational platform that can predict how a drug will behave in the body. Taken from their website “at the start of a typical program, we virtually assay 25 million compounds from a bespoke, focused virtual library of 1 trillion (or more) compounds, against a handful of accurate activity, selectivity and ADME models at a cost of one-one hundredth of a penny per compound, in about one week”. The process is fast enough to search through spaces of 50 trillion compounds in one week (on 10,000 CPUs).
Founded in 2013, Salt Lake City startup Recursion Pharmaceuticals has taken in $15.35 million in funding to support their ambitious goal of finding 100 disease treatments in 10 years. Instead of using the inefficient strategy of studying an explicit molecular target related to a specific disease, they use high-throughput biology, advanced imaging, and artificial intelligence to make many discoveries in parallel. Incredibly, they actually use computer vision to look at cells and identify over 1,000 features that can be used to determine if a sick cell is getting healthier when exposed to 1000s of drug compounds. They’ve partnered with big pharma firm Sanofi, and have a candidate going into clinical trials this year according to an article in MIT Technology Review.
Founded in 2014, Baltimore Maryland startup Insilico Medicine has taken in $10 million in funding so far to tackle aging and age related diseases. Aging and telomeres have been a hot topic lately here on Nanalyze, and Insilico wants to tackle not only aging but also cancer. Don’t we all. Similar to NuMedii, the startup looks at drugs that are already safe to use and see if they can be re-purposed for other uses. Eventually, they’d like to conduct full body digital simulations which kind of sounds like what the most funded AI startup in China is doing, iCarbonX.
Insilico works as a “contract research organization” and they’re using Nvidia GPUs and machine learning algorithms to make this all happen. The AI division of Insilico is called “Pharmaceutical Artificial Intelligence” and they not only have a very nice futuristic website but they also “take pride in keeping our identities away from professional and social networks” and they warn people to “use extreme caution when hiring any of the professionals claiming to work for Deep Pharma or Insilico Medicine“. The “war for talent” is getting a bit heated, no?
Founded in 2012, San Francisco startup Atomwise has taken in $6.35 million in total funding so far from the likes of Khosla Ventures and Draper Fisher Jurvetson. That money has been used to develop AtomNet, “the first structure-based deep convolutional neural network design to predict the bioactivity of small molecules for drug discovery applications“. There’s a pretty heavy white paper you can tuck into for details, but with 27 drug discovery projects launched already with leading research institutions, it sounds like their platform has definitely been validated for usefulness. If you tried to run their algorithms on a typical laptop they would take 10,000 years, but they’re working with IBM cloud and IBM Watson to make it happen at a fraction of the time. They’re also working on confidential projects with Merck and Autodesk as well.
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