Selectivity mechanism of muscarinic acetylcholine receptor antagonism through in silico investigation

Abstract

Structures of muscarinic acetylcholine receptors illustrate the strikingly high degree of homology of the residues among isoforms, thus leading to difficulty in achieving subtype selectivity when targeting these receptors and...

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets

“…Furthermore, most scoring functions used in docking programs are designed for general situations ,, and often ignore the unique traits and binding preferences of individual target proteins . In reverse docking, bias in the scoring functions may arise, favoring proteins with specific properties such as large protein–ligand contact areas or high hydrophobicity. , As a result, docking scores cannot be directly compared in reverse docking. , For example, a study on Glide scores showed that only 57% of ligand–protein relationships were correctly identified mainly due to consistent overestimation or underestimation of scores for certain proteins, a problem known as “interprotein noises”. , Another challenge in target fishing and virtual screening is the discrimination of selectivity among different subtypes within the same protein family. , The high similarity among subtypes makes it difficult to predict the preferred binding of a compound to a specific subtype, which is crucial for effective drug design. − …”