MODEL—molecular descriptor lab: A web‐based server for computing structural and physicochemical features of compounds

Li, Z.R.; Han, Lianyi; Yang, Xue; Yap, Chun Wei; Li, Hengzhuang; Liang-duo, Jiang; Chen, Yu Zong

doi:10.1002/bit.21214

Cited by 48 publications

(26 citation statements)

References 49 publications

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“…In this paper, based on quantum chemistry calculation level of density function theory (DFT) [19,20], Berny energetic gradient and generalized gradient approximation (GGA) were employed to optimize the spatial conformation of coded amino acids. Then, the descriptor calculation web of molecular descriptor lab (MODEL) [21] …”

Section: Principle and Methodologymentioning

confidence: 99%

SVEEVA Descriptor Application to Peptide QSAR

Tong

Che

Liu

et al. 2011

Archiv der Pharmazie

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A new descriptor, SVEEVA (principal component scores vector of electronic eigenvalue descriptors), was derived from principal component analysis (PCA) of a matrix of 220 electronic eigenvalue descriptors of coded amino acids. SVEEVA scales were then applied in three panels of peptide quantitative structure-activity relationship (QSAR) that were modeled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum), cross-validation correlation coefficient (Q²(LOO) were 0.894 and 0.839 for 58 angiotensin-converting enzyme inhibitors, 0.995 and 0.949 for 12 antimicrobial polypeptides, and 0.995 and 0.976 for 20 thromboplastin inhibitors. Satisfactory results showed that information related to biological activity can be systemically expressed by SVEEVA scales, which may be a useful structural expression methodology for study on peptide QSAR.

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Section: Principle and Methodologymentioning

confidence: 99%

SVEEVA Descriptor Application to Peptide QSAR

Tong

Che

Liu

et al. 2011

Archiv der Pharmazie

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“…In this study, Molecular descriptors are calculated using the software MODEL [34] Table S1 of the supporting information.…”

Section: Molecular Descriptors Calculationmentioning

confidence: 99%

Prediction of HIV‐1 Protease Inhibitors Using Machine Learning Approaches

et al. 2009

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In this study, multiple machine learning approaches, including support vector machine (SVM), k-nearest neighbor (k-NN), artificial neural networks (ANN) and logistic regression (LR), are applied for classification of HIV-1 protease inhibitors(PIs) from molecular structure. A diverse set of 641 compounds, including 414 active agents (PIs þ ) and 227 inactive agents (PIs À ), are adopted to develop the classification models. A hybrid feature selection method, which combines Fischers score and Monte Carlo simulated annealing embedded in the support vector machine approach, is used to select the relevant descriptors from a pool of 1559 molecular descriptors. Three validation methods are employed to validate the model in this study. The first one is the five-fold cross validation method and the averaged prediction accuracies for these machine learning approaches are between 83.9 -93.5% for PIsþ and between 67.0 -77.7% for PIsÀ agents. The second validation method is the external test set and the prediction accuracies for PIsþ are between 84.6 -95.2% and for PIsÀ agents are between 63.2 -87.7%. These two validation methods show that the SVM model has better overall performance than other three machine learning models. The third validation method is the yscrambling method, which shows no obvious chance correction in the developed SVM model. The prediction method proposed in this work can give better generalization ability than other recently published methods and can be used as an alternative fast filter in the virtual screening of large chemical database.

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“…These descriptors have been described in the early studies [30,41], including 18 constitutional, 125 topological, 31 quantum chemical, and 25 geometrical descriptors. They are computed from the 3-D structure of each compound by using our own designed molecular descriptor computing program [48,49].…”

Section: Molecular Descriptorsmentioning

confidence: 99%

Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods

Wang

Yang

2008

QSAR Comb. Sci.

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The human Ether-a-Go-Go-Related Gene (hERG) potassium ion channel plays a crucial role in cardiac repolarization. The inhibition of this channel by marketed drug may prolong the length of time between the start of Q wave and end of the T wave on an electrocardiogram (QT interval) and then possibly leads to fatal cardiac arrhythmia. Therefore, it is vital to predict the potential hERG inhibitors in early stages of drug discovery. This work explored the identifying of hERG inhibitors by different machine learning methods including C4.5 Decision Tree (C4.5 DT), multilayer perceptron, Radial Basis Function Network (RBFNetwork), and Support Vector Machine (SVM). Recursive Feature Elimination (RFE), a feature selection method, was used to select molecular descriptors appropriate for distinguishing hERG inhibitors and noninhibitors. For Data_91 classification system with 91 compounds, the prediction accuracies of those methods are 72.1 -92.3% for hERG inhibitors and 81.7 -98.1% for noninhibitors in a five-fold crossvalidation calculation. For an independent validation set of 47 compounds of Data_91, these methods gave an overall accuracy of 72.3 -80.9% using the selected descriptors. This suggests that the combination of the machine learning methods and the feature selection method can provide an effective way to filter the hERG inhibitors in the drug discovery process.

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MODEL—molecular descriptor lab: A web‐based server for computing structural and physicochemical features of compounds

Cited by 48 publications

References 49 publications

SVEEVA Descriptor Application to Peptide QSAR

SVEEVA Descriptor Application to Peptide QSAR

Prediction of HIV‐1 Protease Inhibitors Using Machine Learning Approaches

Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods

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