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

Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia M.; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

doi:10.1007/s10822-014-9808-1

Cited by 18 publications

(16 citation statements)

References 52 publications

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“…To evaluate the quality of the models two approaches were performed according to reference [26]. Simple linear models using only one basic descriptor (boiling point for the first-dimension and logK OW and the logK OW normalized to the weight of the compound for the second-dimension models) were generated for all four responses.…”

Section: Methodsmentioning

confidence: 99%

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

2018

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Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1415-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

2018

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“…In regards to the latter point, viewpoint on targeting AChE as a single target for treating AD is starting to be replaced by the multi-target concept in which the treatment for AD can be approached by a panel of key targets ( Fang et al, 2015 ; Huang et al, 2011 ). Computationally, early studies are predominantly based on simple 2D-QSAR ( Mundy et al, 1978 ; Su & Lien, 1980 ) while later years started to use more sophisticated approach for understanding AChE inhibition encompassing 3D-QSAR ( Deb et al, 2012 ; Lee & Barron, 2016 ; Prado-Prado et al, 2012 ), molecular dynamics ( Shen et al, 2002 ), molecular docking ( Lu et al, 2011 ; Deb et al, 2012 ; Giacoppo et al, 2015 ), pharmacophore modeling ( Lu et al, 2011 ; Gupta & Mohan, 2014 ) and statistical molecular design ( Andersson et al, 2014 ; Prado-Prado, Escobar & Garcia-Mera, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

Simeon

Anuwongcharoen

Shoombuatong

et al. 2016

PeerJ

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Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R2, and values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R2, and values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π–π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

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“…Cross‐validation (CV) is the most extensively employed validation methods . For the predictive power of the model, and accessing the model fit, squared cross‐validation coefficient for leave–one‐out (Q 2 LOO ) and external validation through test set were used . In the LOO cross‐validation (LOO CV ) method, one of the datasets is randomly deleted.…”

Section: Resultsmentioning

confidence: 99%

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

Dadfar

Shafiei

2019

J Chinese Chemical Soc

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A quantitative structure-property relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules and their known properties. In the present study, the applicability of various molecular descriptors is tested for the QSPR study on 60 sulfonamide drugs (sulfa drugs). A training set of 50 sulfa drugs is used to construct QSPR models for the prediction of thermodynamic properties such as enthalpy of formation (ΔH f /kJ mol −1 ) and Gibbs free energy (ΔG f /kJ mol −1 ). These properties are analyzed at the B3LYP/6-31G* level using Gaussian 98 software. The QSPR models were optimized using multiple linear regression (MLR) analysis. The Genetic algorithm and backward method were used to reduce the number of descriptors derived from the Dragon software. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC), and the Durbin-Watson (DW) statistics. The predictive powers of the models are discussed using leave-one-out cross-validation (LOOCV) and external validation methods. The statistical coefficients of the models were found to be satisfactory. Thus, QSPR models derived from this study may be helpful for designing some new sulfa drugs and for predicting their properties.

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

Cited by 18 publications

References 52 publications

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

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