In a recent , Daphne Koller — an artificial intelligence researcher at the Department of Computer Science at Stanford University and, until just a few months ago, chief computing officer at — announced her up-and-coming foray into machine learning-powered drug discovery.
Called insitro (a portmanteau of “in silico” and “in vitro”), the new endeavor looks to stem the increasing costs of developing new drugs and, according to Koller, has already found financial backing from ARCH Venture Partners, Foresite Capital, a16z, and Third Rock Ventures.
“Our hope at insitro is that big data and machine learning, applied to the critical need in drug discovery, can help make the process faster, cheaper, and (most importantly) more successful,” Koller wrote in the post. “To do so, we plan to leverage both cutting-edge [machine learning (ML)] techniques, as well as the profound innovations that have occurred in life sciences, which enable the creation of the large, high-quality data sets that may transform the capabilities of ML in this space.”
Koller, who is also known for being one of the cofounders of the education technology company Coursera, wrote in her post that insitro hopes to offer a new research pathway for a pharmaceutical industry that has already exhausted the “low-hanging fruit” of medications. As drug research begins to turn toward more specialized treatments, smaller market sizes and more ambiguous patient populations are beginning to take a greater and greater toll on the industry, she explained.
“The solution to this problem cannot be that we continue to pay enormous amounts to develop new drugs, most of which fail, and then pass those costs on to our patients,” Koller wrote. “This is neither economically sustainable for society, nor is it ethical, since it prices many new drugs out of reach for many people who need them. We must find a different approach to drug development.”
Koller noted that although ML tech has been steadily advancing and seeing new applications inside and out of healthcare, it’s the increasing access to large, high-quality data sets that will make her team’s drug discovery strategy a success.
“We plan to collect and use a range of very large data sets to train ML models that will help address key problems in the drug discovery and development process,” she wrote. “To enable the machine learning, we will use high-quality data that has already been collected, but we will also invest heavily in the creation of our own datasets using high throughput experimental approaches, datasets that are designed explicitly with machine learning in mind from the very start. The ML models that are developed will then help guide subsequent experiments, providing a tight, closed-loop integration of in silico and in vitro methods.”
Koller used the blog post to put out a call for researchers, engineers, and data scientists, and stressed that her group will be prioritizing full collaboration between the three camps. But even with her ideal workforce and the backing of biotech and tech VCs, the AI guru said that she doesn’t fully see the venture’s implementation of ML as drug discovery’s magic bullet.
“There is a lot of hype today around machine learning, with hyperbolic promises that it will magically solve all of humankind’s problems (and dire warnings that it will lead to the destruction of humankind). We at insitro don’t expect ML to be the solution to all of the problems in drug development, nor to be the magic bullet that helps find a treatment for every disease. However, we do believe that the time is right to rethink the drug design process using a different and more modern toolkit, in the hope that a new paradigm may help us cure more people, sooner, and at a much lower cost,” she concluded.