Machine Learning and Deep Learning Application in Cholinesterase Research Area

As key therapeutic targets for symptomatic treatment of Alzheimer’s disease (AD) according to the cholinergic hypothesis, acetylcholinesterase (AChE; EC 3.1.1.7) and butyrylcholinesterase (BChE; EC 3.1.1.8) have been the subject of numerous studies over decades, leading to large collections of different ligands with corresponding AChE and BChE activity. This vast amount of data provides an ideal basis for the implementation of different machine learning (ML) and deep learning (DL) tools in different steps of the drug discovery process. Mainly applied to identify potential strong inhibitors of AChE and to a lesser extent BChE, many quantitative structure–activity relationship (QSAR) models and other predictive tools have been constructed utilizing different ML algorithms and DL techniques with various success depending on the input data and specific context. Here, we provide an extensive overview of such cases reported in the literature in recent years.