  Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have
demonstrated therapeutic benefit in the treatment of neurological diseases such
as Parkinson's and Alzheimer's, but some also cause dissociative effects that
have led to the synthesis of illicit drugs. The ability to generate NMDAR
antagonists in silico is therefore desirable both for new medication
development and for preempting and identifying new designer drugs. Recently,
generative deep learning models have been applied to de novo drug design as a
means to expand the amount of chemical space that can be explored for potential
drug-like compounds. In this study, we assess the application of a generative
model to the NMDAR to achieve two primary objectives: (i) the creation and
release of a comprehensive library of experimentally validated NMDAR
phencyclidine (PCP) site antagonists to assist the drug discovery community and
(ii) an analysis of both the advantages conferred by applying such generative
artificial intelligence models to drug design and the current limitations of
the approach. We apply, and provide source code for, a variety of ligand- and
structure-based assessment techniques used in standard drug discovery analyses
to the deep learning-generated compounds. We present twelve candidate
antagonists that are not available in existing chemical databases to provide an
example of what this type of workflow can achieve, though synthesis and
experimental validation of these compounds is still required.
