Deep convolutional neural network-based identification and biological evaluation of MAO-B inhibitors

Abstract

Parkinson's disease (PD) is one of the most prominent motor disorder of adult-onset dementia connected to memory and other cognitive abilities. Individuals with this vicious neurodegenerative condition tend to have an elevated expression of Monoamine Oxidase-B (MAO-B) that catalyzes the oxidative deamination of aryalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide. This oxidative stress damages mitochondrial DNA and contributes to the progression of PD. To address this, we have developed a deep learning (DL)-based virtual screening protocol for the identification of promising MAO-B inhibitors using Convolutional neural network (ConvNet) based image classification technique by dealing with two unique kinds of image datasets associated with MACCS fingerprints. Following model building and prediction on the Maybridge library, our approach shortlisted the top 11 compounds at the end of molecular docking protocol. Further, the biological validation of the hits identified 4 compounds as promising MAO-B inhibitors. Among these, the compound RF02426 was found to have >50 % inhibition at 10 μM. Additionally, the study also underscored the utility of scaffold analysis as an effective way for evaluating the significance of structurally diverse compounds in data-driven investigations. We believe that our models are able to pick up diverse chemotype and this can be a starting scaffold for further structural optimization with medicinal chemistry efforts in order to improve their inhibition efficacy and be established as novel MAO-B inhibitors in the furture.