Over the past few
years, many machine learning-based scoring functions
for predicting the binding of small molecules to proteins have been
developed. Their objective is to approximate the distribution which
takes two molecules as input and outputs the energy of their interaction.
Only a scoring function that accounts for the interatomic interactions
involved in binding can accurately predict binding affinity on unseen
molecules. However, many scoring functions make predictions based
on data set biases rather than an understanding of the physics of
binding. These scoring functions perform well when tested on similar
targets to those in the training set but fail to generalize to dissimilar
targets. To test what a machine learning-based scoring function has
learned, input attribution, a technique for learning which features
are important to a model when making a prediction on a particular
data point, can be applied. If a model successfully learns something
beyond data set biases, attribution should give insight into the important
binding interactions that are taking place. We built a machine learning-based
scoring function that aimed to avoid the influence of bias via thorough
train and test data set filtering and show that it achieves comparable
performance on the Comparative Assessment of Scoring Functions, 2016
(CASF-2016) benchmark to other leading methods. We then use the CASF-2016
test set to perform attribution and find that the bonds identified
as important by PointVS, unlike those extracted from other scoring
functions, have a high correlation with those found by a distance-based
interaction profiler. We then show that attribution can be used to
extract important binding pharmacophores from a given protein target
when supplied with a number of bound structures. We use this information
to perform fragment elaboration and see improvements in docking scores
compared to using structural information from a traditional, data-based
approach. This not only provides definitive proof that the scoring
function has learned to identify some important binding interactions
but also constitutes the first deep learning-based method for extracting
structural information from a target for molecule design.