QSPR modeling of bioconcentration factor of nonionic compounds using Gaussian processes and theoretical descriptors derived from electrostatic potentials on molecular surface

Sang, Peng; Zou, Jian‐Wei; Peng, Zhou; Xu, Lin

doi:10.1016/j.chemosphere.2011.01.063

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…20 The application of descriptors derived from the energy of frontier orbitals has a long history in QSAR and QSPR involving small training sets. 21 More recent examples are models for the prediction of bioconcentration factors, 22 rate constants of oxidations of organic contaminants, 23 thiol reactivity and toxicity of water disinfection byproducts, 24,25 and aromatase inhibition. 26 Alternatives to the computationally demanding DFT calculations can be envisaged to obtain energies of frontier orbitals and other valuable properties for whole molecules, bonds, or atoms.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Pereira

Xiao

Latino

et al. 2016

J. Chem. Inf. Model.

168

141

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Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

show abstract

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Pereira

Xiao

Latino

et al. 2016

J. Chem. Inf. Model.

168

141

View full text Add to dashboard Cite

show abstract

“…ChemAxon provides the calculation plugins of invoking function API, so our lab members have made a careful study and repeated experiments. The calculation results are compared with the ones of Gaussian 09 [18], JChem for Excel [34], HyperChem 7.5 [20, 36], and Dragon [37] programs calculation. By invoking the Calculator Plugins and using the Java language, we successfully developed a convenient and available customized batch calculation program (second development software) for the small molecular descriptors.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, several efforts by using computational and mathematical approaches have been made in investigating small molecules of DPP-4 inhibitors. In our previous studies [17], we have attempted to use the quantum chemistry method [18] to optimize a series of DPP-IV inhibitors, and a 2D-QSAR model has been built, which can predict the inhibitory activity of small molecule with satisfying results. However, it is time consuming to calculate the molecular descriptors adopted in 2D-QSAR model.…”

Section: Introductionmentioning

confidence: 99%

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Yang

et al. 2013

BioMed Research International

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The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.

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“…Quantitative structure-property relationship (QSPR) studies have been successfully used for the prediction of physicochemical properties of chemical compounds based on their structures (19)(20)(21)(22)(23)(24). The biological counterpart of such studies, quantitative structure-activity relationships (QSAR), has also been extensively used with great success (25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Density functional theory based quantitative structure-property relationship studies on coumarin-based prodrugs

et al. 2012

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QSPR modeling of bioconcentration factor of nonionic compounds using Gaussian processes and theoretical descriptors derived from electrostatic potentials on molecular surface

Cited by 18 publications

References 26 publications

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Density functional theory based quantitative structure-property relationship studies on coumarin-based prodrugs

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