Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Cytochrome P450 aromatase (CYP19A1)
plays a key role in the development
of estrogen dependent breast cancer, and aromatase inhibitors have
been at the front line of treatment for the past three decades. The
development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role
in the search for promising lead compounds in bioactivity-relevant
chemical space. Here we present a set of comprehensive binding affinity
prediction models for CYP19A1 using our automated Linear Interaction
Energy (LIE) based workflow on a set of 132 putative and structurally
diverse aromatase inhibitors obtained from a typical industrial screening
study. We extended the workflow with machine learning methods to automatically
cluster training and test compounds in order to maximize the number
of explained compounds in one or more predictive LIE models. The method
uses protein–ligand interaction profiles obtained from Molecular
Dynamics (MD) trajectories to help model search and define the applicability
domain of the resolved models. Our method was successful in accounting
for 86% of the data set in 3 robust models that show high correlation
between calculated and observed values for ligand-binding free energies
(RMSE < 2.5 kJ mol–1), with good cross-validation
statistics.