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

Application and Assessment of Deep Learning for the Generation of Potential NMDA Receptor Antagonists