How Computational Chemistry Helps Drug Discovery
Steve Jobs once said that “the biggest innovations of the twenty-first century will be the intersection of biology and technology,” and that “a new era is beginning” that he likened to the digital age that he spent most of his life working in. Numerous examples keep popping up that support his claims. You can now take someone’s DNA, the recipe for their existence, and hand it over to them on a thumb drive in digital form. We’ve even created new DNA letters and expanded the genetic alphabet. We using software programs to create synthetic organisms for the chemical industry that remove reliance on petroleum. We have the ability to edit genes at the germline and potentially remove hereditary diseases. And we’re now able to start modeling how things actually work in nature. One exciting area of work where biology meets technology is computational chemistry.
Simply put, computational chemistry is a branch of chemistry that uses computer simulations to assist in solving chemical problems. It’s not anything new, but the application of machine learning to computational chemistry is creating new opportunities. A paper on the topic describes this as follows:
It’s yet another application of deep learning that’s emerging and now attracting lots of funding, particularly in the area of drug discovery. Computational chemistry can accelerate the long and costly drug discovery process which can be explained by the below diagram taken from a paper on the topic published in the Encyclopedia of Nanotechnology 2015.
The trillion dollar pharmaceutical industry is currently facing a crisis of declining productivity, spending more on research each year, yet achieving fewer breakthroughs per dollar. Computational chemistry promises to decrease time-to-market and lower the overall cost of drug discovery. Consequently, venture capitalists have been pouring money into startups that are using computational chemistry for drug discovery. Let’s take a look at some of them.
While most startups are private companies, not all private companies are startups. We usually consider startups to be recently established companies that have taken in venture capital funding. In the case of Schrödinger, they were established way back in 1990 and have slowly been taking in funding rounds totaling $137 million so far. Their most recent funding round – an $85 million Series E round – closed in January of this year with participation from Google Ventures and the Bill & Melinda Gates Foundation which led three previous investments in Schrödinger since 2010. The well-established firm is working with multiple partner companies to advance a diverse drug discovery pipeline. They also perform extensive research in-house and offer a suite of products for drug discovery and materials science.
The fact that they closed a single funding round this year that exceeded all their past funding rounds combined shows the need to scale their business in the face of increased competition from startups like this next one.
When naming your startup, it’s always fun to take the piss out of your future marketing team by choosing a company name only a handful of people know how to pronounce. That’s what XtalPi decided to do, and if you think their company name is cryptic, just wait until you read about their technology. Founded in 2014 by a group of quantum physicists at MIT, Boston startup XtalPi has taken in $67.5 million in funding to develop “state-of-the-art crystal structure prediction (CSP, also known as polymorph prediction) technology.” A comprehensive paper on how crystal structures relate to drug discovery summarizes the application as follows:
In other words, it’s all about using machine learning and predictive analytics to improve the likelihood that a drug can make it through the drawn-out approval process. The majority of their funding came in the form of a Series B that closed last Fall which followed the announcement of a strategic research collaboration with Pfizer Inc.
