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. But advances in data science - particularly in the areas of deep learning and artificial intelligence (AI) - are fit to fundamentally shift the industry, which means opportunity is ripe for the taking for those enterprises willing to dive in.
For a look at the state of the industry and how data science is already starting to make an impact (plus what the future holds), take a look at the just-released white paper: How Machine Learning is Transforming the Pharmaceutical Industry.
Top Areas of Impact
- R&D: 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. Use cases range from using data science to identify patients for clinical trials, identifying chemical compounds, more advanced computational biochemistry, and more.
- Supply Chain: Identifying the most efficient supply system will become even more important as drugs are increasingly customized to small populations of patients with certain genetic profiles.
- Manufacturing: Drug companies are increasingly able to ensure consistent quality standards with the use of advanced analytics.
- ...and much more! Like, for example, prescription optimization:
Get the Details & Case Studies
Read the full white paper, How Machine Learning is Transforming the Pharmaceutical Industry, for a more in-depth look and case studies. Or, check out the video from Chris Kakkanatt, Data Science Team Leader at Pfizer, who spoke at the EGG2017 conference about orchestrating the organizational and cultural changes necessary to scale the data team at the company.