Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors

Abstract

Recently discovered 42 AChE inhibitors binding at the catalytic and peripheral anionic site were identified on the basis of molecular docking approach, and its comparative quantitative structure-activity relationship (QSAR) models were developed. These structurally diverse inhibitors were obtained by our previously reported high-throughput in vitro screening technique using 384-well plate's assay based on colorimetric method of Ellman. QSAR models were developed using (i) genetic function algorithm, (ii) genetic partial least squares, (iii) support vector machine and (iv) artificial neural network techniques. The QSAR model robustness and significance was critically assessed using different cross-validation techniques on test data set. The generated QSAR models using thermodynamic, electrotopological and electronic descriptors showed that nonlinear methods are more robust than linear methods, and provide insight into the structural features of compounds that are important for AChE inhibition.

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

“…Furthermore, the stronger inhibitors had an asymmetric PAS moiety in terms of principal moment of inertia ( PAS_npr1 ) and lower LUMO energy of the PAS-binding moiety. The importance of a low LUMO energy of the PAS binding moiety corroborates previous findings [ 30 , 45 ] indicating that an aromatic systems with a high reduction potential may be preferential in PAS. It is a known fact that a positive charge—manifested here in a small and more polar vdW area of the CAS-binding moiety—is important for AChE interactions.…”

“…A morpholine in p III was clearly disadvantageous. The benzothiophene and methyl-nitrobenzene substructures has been found before in AChE inhibitors [ 30 ], although not combined with the same moieties presented here, while the isoindolinone-phenyl moiety as a PAS binder is novel. The well-known fact that cationic molecules bind to the CAS region of AChE was corroborated here, since the permanent pyridinium cation was the most potent.…”

“…Similar to our finding here, if compared to other substituents such as piperidinyl and triethylamin, morpholinyl have been shown to be less potent [ 42 , 43 ]. Nevertheless, morpholinyl per se cannot be considered a poor binder of AChE since it is present in inhibitors in the nM to µM range [ 30 , 44 – 46 ].

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“…QSAR investigations of AChE inhibitors for medical applications started to appear in the late 1990s and among the first was a study by Hansch and co-workers [ 11 ] where QSAR-equations based on compounds such as tacrine, carbamates and physostigmine analogues were presented. Since then, many AChE QSAR studies have been presented including carbamates [ 12 – 14 ], analogues of tacrine [ 15 – 18 ], physostigmine [ 19 ], donepezil [ 20 – 24 ], 2,5-piperazinedione [ 25 ], 4-aryl-4-oxo- N -phenyl-2-aminylbutyramide [ 26 ], minaprine [ 27 ], amaryllidaceae alkaloids [ 28 ], and miscellaneous compounds [ 29 , 30 ]. All of these QSAR studies were based on already existing experimental data of molecules not designed for modeling; SMD was not used in any of the studies and no assessments of the descriptor matrices were presented.…”

“…Most commonly, previous studies have resulted in 3D-QSARs [ 13 – 16 , 19 , 21 , 23 , 25 – 27 , 29 ]. 2D-QSARs presented have usually been based on physicochemical descriptors [ 17 , 22 , 24 , 28 , 30 ] and/or topology descriptors [ 18 , 20 ]. The dominating regression method used in these studies was multiple linear regression (MLR) or PLS, but the models were not compared to any reference models and were seldom evaluated with an external test set.…”