1. Goal-oriented research
It’s a long journey to the release of a pharmaceutical product. Of 25,000 preparations in the lab, only 25 on average are tested on humans, five make it to market, and only one of them recoups the cost of investment. Plus, the whole process can take 10 years or more.
With the help of data science, this journey can be shortened. Automated genome sequencing, for example is just one way that artificial intelligence (AI) can help. The use of data science in the analysis of molecule collections and their properties is also of interest. The overarching aim is to classify, understand, and interpret data. With data as a tool in drug development, new predictions and insights are now possible, for example, we could discover why some patients do not respond to a drug or which drug candidates are suitable for further trials.