Introduction: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the
treatment of Parkinson’s disease (PD), Alzheimer’s disease and other neuropsychiatric disorders since
they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A
and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant
efforts have been devoted to finding new compounds with more selectivity and less side effects.
One of the most used approaches is based on the use of computational approaches since they are time
and money-saving and may allow us to find a more relevant structure-activity relationship.
Objective: In this manuscript, we will review the most relevant computational approaches aimed at the
prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask
model aimed at predicting MAO-A and MAO-B inhibitors.
Methods: The QSAR multi-task model herein developed was based on the use of the linear discriminant
analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl.
The molecular descriptors used was calculated using the Dragon software. Classical statistical tests
were performed to check the validity and robustness of the model.
Results: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical
performed tests indicated that this model is robust and reproducible.
Conclusion: MAOIs are compounds of large interest since they are largely used in the treatment of very
serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development
of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and
their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development
of new and more selective MAO inhibitors.