Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

Abstract

Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors

“…Multiple linear regression analysis assumes the existence of linear correlation between molecular descriptors and corresponding bioactivities . The kNN, on the other hand, is a non‐linear nonparametric method that predicts a ligand's bioactivity as distance weighted average of the bioactivities of its k‐nearest neighbors.…”

“…However, 2 separate methodologies were implemented to evaluate Multiple linear regression analysis assumes the existence of linear correlation between molecular descriptors and corresponding bioactivities. [12][13][14][15][16][17] The kNN, on the other hand, is a non-linear nonparametric method that predicts a ligand's bioactivity as distance weighted average of the bioactivities of its k-nearest neighbors. The neighborhood is defined based on certain selected descriptor(s).…”

“…The pharmacophore model(s) can be used as 3D search query(ies) to mine 3D libraries for new SIRT2 inhibitors, while the associated QSAR model(s) can be used to predict the bioactivities of captured hits to prioritize them for in vitro evaluation. Similar approach was used towards the discovery of new mTOR inhibitors, PI3Kγ inhibitors, glucokinase activators, urokinase plasminogen inhibitors, acetylcholinesterase inhibitors, and PPAR‐γ ligands …”

“…[34][35][36][37][38] Since the collected inhibitors originated from several literature sources of different bioassay techniques, we were obliged to use HipHop modeling for assessing the pharmacophoric features of collected inhibitors. [35][36][37][38] The pharmacophoric space of SIRT2 inhibitors was explored through 16 HipHop automatic runs performed on 9 carefully selected training subsets from the collected inhibitors: A, B, C, D, E, F, G, H, and I (see Tables S2-I to S2-IX in supporting information).…”

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

“…Optimal pharmacophores (both structure and ligand based) were validated using the receiver operating characteristic (ROC) curve analysis to assess the ability of each model to correctly classify a group of compounds into actives and inactives (Section S15). Matthews correlation coefficient (MCC) was also undertaken as an additional validation . Additionally, exclusion spheres were added using HIPHOP‐REFINE module of Discovery Studio to improve the ROC properties of QSAR‐guided pharmacophore (Section S8).…”