Quantitative structure–activity relationship of organophosphate compounds based on molecular interaction fields descriptors

Zhao, Jinsong; Yu, Shuxia

doi:10.1016/j.etap.2012.11.018

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…To reduce experimental effort and prepare for other usage of such nerve agents, theoretical research on predicting the vapor pressure and/or toxicity is suggested as a key breakthrough in response to such attacks. Although very few studies have been performed in this regard, several recent prediction-based studies, such as those on the quantitative structure–activity relationship (QSAR) and machine learning (ML) techniques, have been reported for determining the oral toxicities of nerve agents and predicting the vapor pressures of pesticides, including some organophosphorus materials. − While pioneering QSAR models have been developed to predict the acute oral toxicities of organophosphorus compounds, − the dataset used had less than 50 compounds; more recently, a study with ML models on a dataset with 456 compounds using 265 descriptors has been reported, which still had low interpretability and accuracy. This is attributed to several factors during the construction of the regression model that affect oral toxicity.…”

Section: Introductionmentioning

confidence: 99%

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update

Jeong

Lee

Woo

et al. 2022

Chem. Res. Toxicol.

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The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

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Section: Introductionmentioning

confidence: 99%

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update

Jeong

Lee

Woo

et al. 2022

Chem. Res. Toxicol.

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“…Of course, while both examples were interpreted in the light of spectroscopic datasets like those used in metabolomics [22, 23], MB-OPLS is a fully general algorithm that admits any multiblock dataset for the purposes of regression or discriminant analysis. For example, recent applications of MB-PLS for investigating food spoilage [28], iron-ore content [29], chemical toxicity [30], the evolution of human anatomy [31], and the assessment of cortical and muscle activity in Parkinson's disease patients [32] would benefit from our MB-OPLS algorithm. The presented algorithm admits either a vector or a matrix as responses, and is implemented in the latest version of the open-source MVAPACK chemometrics toolbox [24].…”

Section: Discussionmentioning

confidence: 99%

A sequential algorithm for multiblock orthogonal projections to latent structures

Worley

Powers

2015

Chemometrics and Intelligent Laboratory Systems

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Methods of multiblock bilinear factorizations have increased in popularity in chemistry and biology as recent increases in the availability of information-rich spectroscopic platforms has made collecting multiple spectroscopic observations per sample a practicable possibility. Of the existing multiblock methods, consensus PCA (CPCA-W) and multiblock PLS (MB-PLS) have been shown to bear desirable qualities for multivariate modeling, most notably their computability from single-block PCA and PLS factorizations. While MB-PLS is a powerful extension to the nonlinear iterative partial least squares (NIPALS) framework, it still spreads predictive information across multiple components when response-uncorrelated variation exists in the data. The OnPLS extension to O2PLS provides a means of simultaneously extracting predictive and uncorrelated variation from a set of matrices, but is more suited to unsupervised data discovery than regression. We describe the union of NIPALS MB-PLS with an orthogonal signal correction (OSC) filter, called MB-OPLS, and illustrate its equivalence to single-block OPLS for regression and discriminant analysis.

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“…Although QSAR models to predict K i (Johnson et al 1985;Plyamovatyi et al 1997;Ruark et al 2013;Lee and Barron 2016) and median lethal dose (LD 50 ) values (Devillers 2004;Bermúdez-Saldaña and Cronin 2006;García-Domenech et al 2007;Zhao and Yu 2013;Camacho-Mendoza et al 2018;Ding et al 2018) have been reported, these models suffer from small datasets, insufficient structural structure diversity, oversimplified modeling algorithms, and the lack of both strict validation and applicability domain (AD) evaluations; thus, their practical use is limited. For example, based on semi-empirical quantum chemical (QC) descriptors, Johnson et al (1985) constructed linear QSAR models of 19 OPs with their hydrolysis rate constants using a stepwise linear regression analysis method.…”

Section: Introductionmentioning

confidence: 99%

“…Although the model exhibits a predictive ability to a certain extent (the determination coefficient of the external test set was 0.66), it is relatively complex (the number of descriptors is 62), thus hampering its interpretability. Based on molecular interaction field descriptors, Zhao and Yu (2013) built a three-dimensional (3D) QSAR model for predicting the acute toxicity of 35 OPs to houseflies, finding that the hydrophobicity and hydrogen-bond receptors and donors of OPs play important roles in their interactions with AChE. Camacho-Mendoza et al (2018) constructed linear QSAR models for identifying the acute toxicity of 25 OPs that were based on QC descriptors and employed density functional theory (DFT), revealing that the molecular volume, the most negative atomic charge, and the highest occupied molecular orbital (HOMO) energy were closely related to acute toxicity.…”

Section: Introductionmentioning

confidence: 99%

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

Wang¹,

Ding²,

Shi³

et al. 2021

Arch Toxicol

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Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

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Quantitative structure–activity relationship of organophosphate compounds based on molecular interaction fields descriptors

Cited by 12 publications

References 25 publications

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update

A sequential algorithm for multiblock orthogonal projections to latent structures

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

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