This article was published as a part of the Data Science Blogathon. This problem that we will discuss in this blog comes from the cutting-edge intersection of AI with the drug discovery process, where DataRobot and my team play a very significant role. This blog is focused on an engagement my team, and I did with one of our largest customers, a top-tier pharmaceutical company in the United States. The goal with this type of work my team and I do is to tackle problems that are not standardized, which allows us to learn from them and then cross-functionally work with our Product and Engineering teams to integrate them into the DataRobot Platform, which pushes the boundaries of innovation in AI. In this blog, I consider an example and look into some work I have done in this field where I marry classical approaches in Survival Analysis with modern-day machine learning techniques to improve explainability, improve the accuracy of predicting adverse health events in patients and decrease time to release of the drug in the market.

One of the key issues with drug discovery is dealing with uncertainty on outcome timing, having few observations in your patient samples, and being overwhelmed with features. In this article I discuss some of the data science methods used previously and new approaches my team and I have been able to take resulting in success. Using AI for drug discovery is one of the most exciting domains that is already starting to impact and improve our society. Suppose you are asked to compare proportions-risks, rates, etc… between different groups. If you are in the realm of statistical testing, you will be using a chi-square or the Fisher-Exact test, or Logistic Regression and allied models beyond that.