How Machine Learning
is Transforming

Pharmaceuticals

... and Why AI is Next

The pharmaceutical industry may very well be at a crossroads. In many ways, it is a victim of its own success. After a century of rapid progress in the development of new medications,the discovery of new drugs has slowed significantly and the process of developing new pharmaceuticals has become more and more expensive.


Simply put, pharmaceutical researchers have already picked all of the low-hanging fruit. At the same time, the regulatory environment has become more challenging, demanding far more extensive testing before drugs can go to market.

The good news is that advances in data science offer an opportunity to fundamentally shift the paradigm, leading to better and more affordable medications.

One of the most obvious areas of opportunity for data science in the drug industry comes in research and development, which serves as the sector’s foundation. In the U.S., companies spent $50 billion on R&D in 2015, while in Europe spending hit €30 billion (source). Therefore, for drug companies, there is major incentive to reduce R&D spending, both to free up funds for additional ventures as well as to be able to offer lower prices for their products.


There are a number of ways that sophisticated data science can help researchers save money and time in R&D. But on top of that, there are also more traditional ways - like in the supply chain and in manufacturing - where there is room for development in the world of machine learning and AI.