Exploring different virtual screening strategies for acetylcholinesterase inhibitors

Area of Science:

• Computational chemistry
• Drug discovery
• Medicinal chemistry

Background:

• Acetylcholinesterase (AChE) is a key target for treating Alzheimer's disease.
• Virtual screening is crucial for identifying novel AChE inhibitors.
• Evaluating different computational strategies is essential for optimizing screening efficiency.

Purpose of the Study:

• To compare the effectiveness of various computational methods for virtual screening of AChE inhibitors.
• To identify the most accurate and efficient strategy for identifying potential AChE inhibitors.

Main Methods:

• Utilized molecular docking, similarity search (shape-based and electrostatic), and pharmacophore modeling.
• Employed a dataset of known AChE inhibitors and decoys from the Directory of Useful Decoys (DUD).
• Assessed model performance using enrichment factor (EF) and receiver operating curve (ROC) analysis.

Main Results:

• The electrostatic similarity search protocol (ET_combo) demonstrated superior performance, achieving >95% enrichment in the top 1% and 2% of the dataset with an AUC of 0.958.
• Shape-based similarity search protocols (ROCS, PHASE) also showed satisfactory results.
• Molecular docking protocols exhibited poor performance, with enrichment factors below 30%.

Conclusions:

• Electrostatic and shape-based similarity search methods are highly effective for virtual screening of AChE inhibitors.
• These methods provide a more plausible and efficient solution compared to molecular docking for identifying AChE inhibitors.
• The findings highlight the utility of similarity searching in accelerating drug discovery for AChE-related targets.