How Machine Learning Can Unlock Cures For Tomorrow's Diseases Posted on : May 05 - 2021

In February 2021, NASA officials and people around the world celebrated the wildly successful landing of the Mars Perseverance rover. We all applauded the scientists and engineers responsible for this incredible achievement, which built on 80 years of multidisciplinary advances in physics, chemistry, mechanical engineering and materials science — and previous achievements such as the moon landing.

In the field of AI-driven drug discovery, we are still seeking our own proverbial moon landing — namely, understanding and curing a major disease in a data-driven way.

Succeeding in AI-driven cures will vastly improve and save the lives of billions of people throughout the planet now and into the future. But to do so, we must move past our discipline’s usual boundaries and comfort zones and work collaboratively across fields — which means software engineers must delve into biology and genomics, and scientists must adopt new technologies, like machine learning, that could have previously appeared distant to them.

I believe machine learning is at the heart of this coming revolution in drug discovery, just as it has been pivotal in many other industries. From text translation and image classification to credit risk assessment, advertising optimization and shopping item recommendation, we take these advances in machine learning for granted in our daily lives. But if we apply similar data-driven thinking in drug discovery, we could unlock hidden patterns in biological data that will allow us to understand diseases and produce cures thought previously unreachable.

Furthermore, this might be AI’s most impactful area yet, and here’s why.

First, the amount of biological data is increasing exponentially due to the decreasing cost associated with generating it. Due to advances in genomic sequencing throughput, it’s much more cost-efficient to adopt and operate sequencing machinery; in effect, pharmaceutical biotech companies, large academic initiatives and startups alike have been able to use this technology to generate data at a large scale.

However, since data from sequencing tends to be extremely high-dimensional (think an entire hard drive worth of data per blood draw), elementary observational statistics or standard algorithms can’t accurately detect the patterns that are critical for understanding how diseases work. But where these methods have failed to reach the best accuracies, machine learning has helped fill in the blanks and see the patterns previously invisible. View More