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

Acetylcholinesterase (AChE) and butyrylcholines-terase (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 similar to 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 -perform-ing 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 mu M). 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.

Automated summary

In this study, machine learning models were developed to predict novel AChE and BChE inhibitors. The models showed good performance in virtual screening and increased the hit rate of assay. A total of 88 novel AChE and 126 novel BChE inhibitors were identified, with significant inhibitory effects. Structure-activity relationship analysis revealed scaffolds for chemistry design and optimization.