A Survey of Quantitative Descriptions of Molecular Structure

Guha, Rajarshi; Willighagen, Egon

doi:10.2174/1568026611212180002

Cited by 15 publications

(10 citation statements)

References 34 publications

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“…More recent quantitative structure activity relationship (QSAR)-based approaches have advanced the use of sophisticated molecular descriptors and/or complex algorithms to improve predictive capability (e.g. Guha and Willighagen, 2012, Kim et al, 2016, Papa et al, 2009, Schüürmann et al, 2006, Svetnik et al, 2003, Tetko et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments

et al. 2018

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Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (K), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of K and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.

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Section: Introductionmentioning

confidence: 99%

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments

et al. 2018

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“…Constitutional descriptors usually represent counts of different types of atoms or bonds. While very simplistic, they play a useful role in predictive modeling . Among the constitutional descriptors, nRotB exhibited the highest (100%) contribution.…”

Section: Resultsmentioning

confidence: 99%

QSTR Modeling for Qualitative and Quantitative Toxicity Predictions of Diverse Chemical Pesticides in Honey Bee for Regulatory Purposes

Singh

Basant²,

Mohan

2014

Chem. Res. Toxicol.

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Pesticides are designed toxic chemicals for specific purposes and can harm nontarget species as well. The honey bee is considered a nontarget test species for toxicity evaluation of chemicals. Global QSTR (quantitative structure-toxicity relationship) models were established for qualitative and quantitative toxicity prediction of pesticides in honey bee (Apis mellifera) based on the experimental toxicity data of 237 structurally diverse pesticides. Structural diversity of the chemical pesticides and nonlinear dependence in the toxicity data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) QSTR models were constructed for classification (two and four categories) and function optimization problems using the toxicity end point in honey bees. The predictive power of the QSTR models was tested through rigorous validation performed using the internal and external procedures employing a wide series of statistical checks. In complete data, the PNN-QSTR model rendered a classification accuracy of 96.62% (two-category) and 95.57% (four-category), while the GRNN-QSTR model yielded a correlation (R(2)) of 0.841 between the measured and predicted toxicity values with a mean squared error (MSE) of 0.22. The results suggest the appropriateness of the developed QSTR models for reliably predicting qualitative and quantitative toxicities of pesticides in honey bee. Both the PNN and GRNN based QSTR models constructed here can be useful tools in predicting the qualitative and quantitative toxicities of the new chemical pesticides for regulatory purposes.

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“…Although topological descriptors are extensively used in predictive modelling, they are usually hard to interpret 25 . Topological descriptors may have provided information on how well a molecule ts in the binding site and along with atom counts the interactions with the binding residues.…”

Section: Feature Importancementioning

confidence: 99%

Random forest classification for predicting lifespan-extending chemical compounds

Kapsiani

Howlin

2021

Preprint

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Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve (AUC) score of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1,738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (i) flavonoids, (ii) fatty acids and conjugates, and (iii) organooxygen compounds.

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A Survey of Quantitative Descriptions of Molecular Structure

Cited by 15 publications

References 34 publications

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments

QSTR Modeling for Qualitative and Quantitative Toxicity Predictions of Diverse Chemical Pesticides in Honey Bee for Regulatory Purposes

Random forest classification for predicting lifespan-extending chemical compounds

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