OBJECTIVES: Monoamine oxidase (MAO) enzymes, specifically MAO-A and MAO-B, are crucial role in breaking monoamine neurotransmitters. Therefore, MAO inhibitors (MAOIs) have a crucial role in treating various neurodegenerative disorders, including Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In the present study, we developed a novel cheminformatics pipeline by generating three diverse molecular feature-based machine learning-assisted quantitative structural activity relationship (ML-QSAR) models concerning the MAO-B inhibition mechanism.

METHODS: We implemented PubChem fingerprints, substructure fingerprints, and 1D&2D molecular descriptors to unravel the structural insights responsible for decoding the origin of MAO-B inhibition among 249 non-reductant molecules.

RESULTS: Based on a random forest ML algorithm, the final PubChem fingerprint, substructure fingerprint, and 1D&2D molecular descriptor prediction models demonstrated significant robustness by achieving correlation coefficients of 0.9863, 0.9796, and 0.9852, respectively. The significant features of each predictive model responsible for MAO-B inhibition were extracted through a comprehensive variance importance plot (VIP) and correlation matrix analysis. The final predictive models were further developed as a web app, MAO-B-pred (https://mao-b-pred.streamlit.app/), to allow users to predict the bioactivity of molecules against MAO-B. Molecular docking and dynamics studies were further conducted to gain insight into the atomic-level molecular interactions between ligand-receptor complexes.

CONCLUSIONS:

The findings were compared with the structural features obtained from ML-QSAR models, which support the mechanistic understanding of the binding phenomena. The presented models have the potential to serve as tools for identifying crucial molecular characteristics in the rational design of MAO-B target inhibitors, which might be employed for developing effective drugs concerning neurodegenerative disorders.