Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase

Veselinović, Jovana B.; Nikolić, Gordana; Trutić, Nataša; Živković, Jelena; Veselinović, Aleksandar M.

doi:10.1080/1062936x.2015.1049665

Cited by 37 publications

(10 citation statements)

References 37 publications

(57 reference statements)

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“…Furthermore, Lee & Barron (2016) performed a 3D-QSAR investigation on a large set of 341 compounds comprising of organophosphates and carbamates. Moreover, Veselinović et al (2015) compiled a set of 278 organophosphates for which they developed QSAR models for predicting AChE inhibition using SMILES-based descriptors.…”

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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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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“…According to many authors, a rational split into training and validation set gives better statistical results of the validation sets than models based on random splits [54]. However, the experiment confirms that there are splits successful for one approach, which are unsuccessful for another approach [55][56][57][58][59]. For example, three different splits (Table 1) into training and validation sets of 87 anticancer inhibitors [60] give models with different predictive abilities ( Table 2).…”

Section: The First Weirdness Of Qspr/qsarmentioning

confidence: 91%

QSPR/QSAR: State-of-Art, Weirdness, the Future

Toropov

Toropova

2020

Molecules

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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“…It is possible to calculate the correlation weights, which establish correlation between the optimal descriptor and the desired endpoint. The calculation can be done by the Monte Carlo method [19][20][21][22][23][24][25][26][27][28][29].…”

Section: Optimal Descriptorsmentioning

confidence: 99%

“…The Correlation and Logic (CORAL) software can be used as a computational tool to carry out calculations to build up the QSPR/QSAR based on molecular descriptors from SMILES notation. All QSAR models are based on the Monte Carlo approach according to the principle "QSAR is a random event" [19][20][21][22][23][24][25][26]. The aim of the present study was to test the predictive potential of the CORAL models for prediction of toxicity of a large group of organic compounds to D. magna.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo–based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna

Toropova

Toropov

Veselinović

et al. 2016

Enviro Toxic and Chemistry

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Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.

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Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase

Cited by 37 publications

References 37 publications

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

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

QSPR/QSAR: State-of-Art, Weirdness, the Future

Monte Carlo–based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna

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