aug-MIA-QSPR modelling of the toxicities of anilines and phenols to Vibrio fischeri and Pseudokirchneriella subcapitata

Guimarães, Maria Carolina; Mota, Estella G. da; Silva, Daniel G.; Freitas, Matheus P.

doi:10.1016/j.chemolab.2014.03.005

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…The key difference between molecule 17 and the cluster of active compounds (C1) lies in substituent positions X (O for NH) and R6 (propenyl group for H), while the inactive compounds in cluster C2 (12,13,15,16,18) substituent position R5 (none possesses a methoxy group). Note the greater proximity of molecule 17 to C2 rather than C1, which suggests greater structural resemblance to the former.…”

Section: Results and Discussion 31 Principal Component Analysismentioning

confidence: 99%

“…The neolignans are dimers formed from oxidative coupling of allyl and propenil phenols that occur in the Myristicaceae family of plants. The neolignans employed in this paper are those of the type 8.O.4′, with an O bridge between carbons 8 and 4 [20], where five have been reported to be active (5,6,8,9,17) and 13 are reported to be inactive (1,2,3,4,7,10,11,12,13,14,15,16,18) to S. mansoni.…”

Section: Methodsmentioning

confidence: 99%

“…In the former, atom types are randomly assigned dissimilar colours and their sizes designed to be proportional to the Van der Waals radius, while in the latter, the colours are carefully selected so that their pixel values (according to the RGB system) possess perfect correlation with Pauling's electronegativity for atoms. Note that solid colours are employed in both schemes to guarantee proper correspondence with the selected atomic property [17,18]. It is anticipated that the proposed extensions aid in yielding MIA-QSAR models with greater interpretability and predictive power.…”

Section: Introductionmentioning

confidence: 99%

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Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

Duarte

Barigye

Mota

et al. 2015

SAR and QSAR in Environmental Research

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Theoretical models for exploring the antischistosomal activity of a dataset of 18 synthetic neolignans are built using the multivariate image analysis applied to structure-activity relationships (MIA-SAR) approach. The obtained models were validated using the accuracy (Acc) in leave-one-out cross-validation, external validation and Y-randomization procedures, yielding correct classification superior to 80%, 70% and 60%, respectively. Additionally, a comparison was made of the models obtained from binary (black and white) and coloured images; the colours (pixel values) were selected to correspond to chemical properties. It was observed that the models obtained from coloured images with pixel values corresponding to electronegativity (known as the aug-MIA-SAR(colour) approach) generally yielded superior statistical parameters compared with those obtained from binary images (MIA-SAR) and randomly coloured images (atoms are coloured according to their type) with atomic sizes corresponding to Van der Waals radius (aug-MIA-SAR), respectively. Mechanistic interpretation of the influence of different substituents on the antischistosomal activity revealed that methoxy substituents in the R1 (or R2) and R5 positions of the neolignan scaffold are indispensable for the antischistosomal activity. The obtained results provide knowledge of the possible structural modifications to yield novel neolignan compounds with antischistosomal activity.

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Section: Results and Discussion 31 Principal Component Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

Duarte

Barigye

Mota

et al. 2015

SAR and QSAR in Environmental Research

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“…MIA-QSPR (multivariate image analysis applied to quantitative structure–property relationship) modeling is another technique applied to predict properties, providing models with satisfactory predictive capability. 27 Its main advantage over most of the structure based methodologies lies on the no need for conformational screening and 3D alignment; it just requires a 2D alignment, which refers to simply superimpose 2D images–2D chemical structures drawn with the aid of an appropriate drawing program.…”

Section: Introductionmentioning

confidence: 99%

PLS and N-PLS based MIA-QSPR modeling of the photodegradation half-lives for polychlorinated biphenyl congeners

2020

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“…It is a crucial step to obtain the significant chemical information in QSAR research. Recently, a kind of QSAR methods based on the conventional images of molecular structures has been proposed, such as multivariate image analysis (MIA‐QSAR), aug‐MIA‐QSAR, and 2D‐discrete Fourier transform strategy . The obtained results indicate that the descriptors derived from the pixel points in the images of molecular structures have done well, although the intuitive physicochemical significances of the variables in models are not clear enough.…”

Section: Introductionmentioning

confidence: 99%

Application of image moments in MIA‐QSAR

Wang

et al. 2017

Journal of Chemometrics

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Owing to the presence of some significant chemical information, the conventional images of molecular structures have been used in the studies of quantitative structure-activity relationship by multivariate image analysis (MIA-QSAR). In this contribution, we suggest that the Tchebichef moments (TMs) calculated directly from the grayscale images of molecular structures are used as molecular descriptors to build linear QSAR models by stepwise regression. The proposed approach was applied to QSAR research on a series of HIV-1 non-nucleoside reverse transcriptase inhibitors, and satisfactory results were obtained. Compared with several published methods, the results indicate that the TM method possesses higher accuracy and reliability. The TMs effectively decompose the image information of molecular structures at different levels without any pretreatment owing its very favorable multiresolution, holographic, and inherent invariance properties. Our study successfully extends the application of image moments to MIA-QSAR research.

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aug-MIA-QSPR modelling of the toxicities of anilines and phenols to Vibrio fischeri and Pseudokirchneriella subcapitata

Cited by 15 publications

References 17 publications

Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

PLS and N-PLS based MIA-QSPR modeling of the photodegradation half-lives for polychlorinated biphenyl congeners

Application of image moments in MIA‐QSAR

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