Mordred: a molecular descriptor calculator

Moriwaki, Hirotomo; Tian, Yinbao; Kawashita, Norihito; Takagi, Tatsuya

doi:10.1186/s13321-018-0258-y

Cited by 879 publications

(825 citation statements)

References 25 publications

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“…We used Mordred to calculate 1,612 2D descriptors for the collected compounds. Compound‐specific properties, such as physicochemical, molecular modeling, and structural properties in both the non‐curated and the curated datasets were obtained.…”

Section: Methodsmentioning

confidence: 99%

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki

Watanabe

Kawashima

et al. 2018

Molecular Informatics

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A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance ( CL int ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

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Section: Methodsmentioning

confidence: 99%

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki

Watanabe

Kawashima

et al. 2018

Molecular Informatics

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“…To calculate the structure descriptors, the 2D descriptors of RDKit, Mordred, and DRAGON, whose version was 7.0, were used. Each descriptor set was used to construct regression models independently.…”

Section: Resultsmentioning

confidence: 99%

Estimation of predictive performance for test data in applicability domains using y‐randomization

Kaneko

2019

Journal of Chemometrics

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A new measure of predictive performance for objective variables in regression analysis is proposed, enabling the y-errors of new samples or test samples to be estimated in the applicability domains (ADs) of regression models. The proposed measure, based on y-randomization, considers chance correlations and is calculated using only training data. This chance correlation-excluded mean absolute error (MAE CCE ) can estimate the y-errors of new samples considering the influence of chance correlations in the given dataset on the regression models. Experiments using numerical simulation, quantitative structureactivity relationship, and quantitative structure-property relationship datasets confirm that MAE CCE can estimate the distribution of y-errors of new samples in ADs for various training datasets, descriptor sets, and regression analysis methods, enabling chance correlations to be eliminated from data analysis.Python and MATLAB codes for the proposed algorithm are available at https://github.com/hkaneko1985/maecce.

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“…A molecular descriptor is defined as the final result of a logical and mathematical method, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment. DRAGON software is very important software for the calculation of a lot of descriptors including constitutional, 2D autocorrelations, topological, getaway, WHIM, geometrical, RDF, and 2D matrix‐based descriptors . They are derived from different theories and approaches with the aim of predicting the biological and physicochemical properties of molecules .…”

Section: Methodsmentioning

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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Mordred: a molecular descriptor calculator

Cited by 879 publications

References 25 publications

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Estimation of predictive performance for test data in applicability domains using y‐randomization

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

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