Showing posts from July, 2019

How Good Could (Should) My Models Be?

One factor that is often overlooked when building a predictive model is the impact of the experimental error on the performance of the model.  In this post, we will examine one technique for estimating the impact of the experimental error on correlation and estimating the maximum possible correlation that can be achieved with a particular dataset.   This post was motivated by an excellent series, called Bucket List Papers , that my friends at  MedChemica recently initiated. In this series, they are highlighting 100 important papers in Medicinal and Computational Chemistry.  All of the papers they’ve featured so far have been classics. I’d recommend their Bucket List as essential reading for anyone involved in Cheminformatics, Computer-Aided Drug Design, or Medicinal Chemistry. I’m not quite so ambitious, but I would like to use this post to highlight an important and frequently overlooked paper.  As usual, we’ll also look at some code to implement the method described in the