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.

简介：单胺氧化酶抑制剂（MAOI）是主要用于治疗帕金森氏病（PD），阿尔茨海默氏病和其他神经精神疾病的化合物，因为它们与MAO酶活性密切相关。MAO酶的两种同工型MAO-A和MAO-B负责单胺神经递质的降解，因此，相关工作已致力于找到具有更高选择性和更少副作用的新化合物。一种最常用的方法是基于计算方法，因为它们既省时又省钱，并且可以使我们找到更相关的结构-活动关系。

目的：在本文中，我们将回顾旨在预测和开发新型MAO抑制剂的最相关的计算方法。随后，我们还将介绍一种旨在预测MAO-A和MAO-B抑制剂的新的多任务模型。

方法：本文开发的QSAR多任务模型基于线性判别分析的使用。该模型的开发是从公共数据集Chembl中收集了5,759种化合物。使用Dragon软件计算所使用的分子描述符。进行经典的统计检验以检查模型的有效性和鲁棒性。

结果：本文提出的模型能够正确分类所有5,759种化合物。所有统计执行的测试均表明该模型是可靠且可重现的。

结论：MAOI是备受关注的化合物，因为它们被广泛用于治疗非常严重的疾病。这些抑制剂可能会失去功效并产生严重的副作用。因此，选择性MAO-A或MAO-B抑制剂的开发对于这些疾病及其作用的治疗至关重要。本文提出的多目标QSAR模型可能是开发新的和更具选择性的MAO抑制剂的相关工具。