Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening

Acetylcholinesterase (AChE) and butyrylcholinesterase
(BChE) play
important roles in human neurodegenerative disorders such as Alzheimer’s
disease. In this study, machine learning methods were applied to develop
quantitative structure–activity relationship models for the
prediction of novel AChE and BChE inhibitors based on data from quantitative
high-throughput screening assays. The models were used to virtually
screen an in-house collection of ∼360K compounds. The optimal
models achieved good performance with area under the receiver operating
characteristic curve values ranging from 0.83 ± 0.03 to 0.87
± 0.01 for the prediction of AChE/BChE inhibition activity and
selectivity. Experimental validation showed that the best-performing
models increased the assay hit rate by several folds. We identified
88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE)
of which showed potent inhibitory effects (IC50 < 5
μM). In addition, structure–activity relationship analysis
of the BChE inhibitors revealed scaffolds for chemistry design and
optimization. In conclusion, machine learning models were shown to
efficiently identify potent and selective inhibitors against AChE
and BChE and novel structural series for further design and development
of potential therapeutics against neurodegenerative disorders.