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At Bayer, one of the largest pharmaceutical companies in the world, gaining a deep understanding of biological systems is paramount for the discovery of new therapeutics. This is inspiring the adoption of technologies that can accelerate and automate discovery, spanning all of the components of their data infrastructure, starting with the database.
The challenges posed within the data and discovery process are not unique to Bayer:
Earlier this month, we were joined by Natalie Kurbatova, Associate Principle Scientist at AstraZeneca on the first seres of Grakn Orbit.
Our newest community event brings members of the #GraknCommunity to the forefront. Through tech panels, presentations, use case deep dives, and more, our community shares the latest and greatest from the Grakn Cosmos.
There’s a common refrain among the chronically disappointed, it goes a little something like this: “if this is the future, where is my jetpack?” Juxtaposing this longing for a retro-future against the wonder-world of ubiquitous computing, programmable cells, and renascent space exploration can make the gripe sound out-of-sorts on a cursory examination. For some people, this misplaced nostalgic futurism can be remarkably persistent. This causes a tendency to cling to predictions that look quaint in retrospect, ignoring the astounding reality that nobody could have predicted. However, with deep learning for drug discovery, we are now able to predict so much more! Which is of great importance in the pharmaceutical industry.
Applied to artificial intelligence, a person with this demeanor might adapt their complaint along the lines of “it’s almost 8 years since AlexNet, where is my self-driving car/AI-mediated utopia/repressive AI overlords?” It can indeed seem like the expectations of the mid-2010s have gone unmet, and predictions of a next AI winter are gaining steam among pessimists. The goal of this essay is to discuss meaningful machine learning progress in the real-world application of drug discovery. I hope to convince you to consider yet another old adage, this one from AI researchers, paraphrased slightly: “AI is only AI until it works, after that it’s just software.”