Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics Approach

[Image: see text] The monoamine oxidase (MAO) enzyme class is a prevalent target for many neurodegenerative and depressive disorders. Even though scrutinization of many promising drugs for the treatment of MAO inhibition has been carried out in recent times, a conclusive structural requirement for potent activity needs to be developed. Numerous approaches have been examined for the identification of structural features for potent MAO inhibitors (MAOIs) that mainly involve an array of computational studies, synthetic approaches, and biological evaluation. In this paper, we have analyzed ∼2200 well-known MAOIs to expand perceptions in the chemical space of MAOIs. The physicochemical properties of the MAOIs disclosed a discernible hydrophobic feature making a bunch discrete from the central nervous system (CNS) acting drugs, as exposed using the principal component analysis (PCA). The Murcko scaffold structure study revealed unfavorable and favorable scaffold structures, in both data sets, with the highest biological activity shown by the 3-phenyl-2H-chromen-2-one scaffold. This scaffold showed a polypharmacological effect. R-group disintegration and automatic structure–activity relationship (SAR) study resulted in identification of substructures responsible for the inhibitory bioactivity of the MAO-A and MAO-B enzymes. Moreover, with activity cliff analysis, significant biological activity was detected by simple molecular conversion in the chemical compound structure. In addition, we used the machine learning tool to generate a hypothesis wherein pyrazole, benzene ring, and amide containing structural functionalities can exhibit potential biological activities. This hypothesis revealed that CNS target drugs, C4155, C13390, C21265, C43862, C31524, C24810, C37100, C42075, and C43644, could be repurposed as valuable candidates for the MAO-B enzyme. For researchers, this study will bring new perceptions in the discovery and development of MAOIs and direct lead and hit optimization for the progress of small molecules beneficial for MAO-targeting associated diseases.