Gaussian Process Regression on Molecules in GPflow

This post demonstrates how to train a Gaussian Process (GP) to predict molecular properties using the GPflow library by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprints. In this example, we'll be trying to predict the experimentally-determined electronic transition wavelengths of molecular photoswitches, a class of molecule that undergoes a reversible transformation between its E and Z isomers upon irradiation by light. We'll start by importing all of the machine learning and chemistry libraries we're going to use. For our molecular representation, we're going to be working with the widely-used Morgan fingerprints. Under this representation, molecules are represented as bit vectors.