Prediction of Depuration Rate Constants for Polychlorinated Biphenyl Congeners

Yu, Xinliang

doi:10.1021/acsomega.9b02072

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Other two Burden eigenvalues (SpMax3_Bh(m), SpMax5_Bh(m)) have also been among the most important descriptors correlating with the inhibition of c-src. SpMax3_Bh(m) has been used in predicting depuration rate constants for environmental pollutants of the polychlorinated biphenyls group [45], and the less relevant (in our case) SpMax6_Bh(m) has been used to predict chronic toxicity of substances to Pseudokirchneriella subcapitata [46]. The second most important descriptor for our data set was DECC (eccentric topologic index), which has been previously reported to be important in the prediction of monoamine oxidase A (MAO-A) activity [47,48], placental barrier permeability [49], and gas chromatographic retention times [50].…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

IJMS

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A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

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Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

IJMS

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show abstract

“…Other two Burden eigenvalues (SpMax3_Bh(m), SpMax5_Bh(m)) have also been among the most descriptors correlating with the inhibition of c-src. SpMax3_Bh(m) has been used in predicting depuration rate constants for environmental pollutants of the polychlorinated biphenyls group (87), and the less relevant (in our case) SpMax6_Bh(m) has been used to predict chronic toxicity of substances to Pseudokirchneriella subcapitata (88). The second most important descriptor for our data set was DECC (eccentric topologic index) has been previously reported to be important in the prediction of MAO-A activity (89,90) , placental barrier permeability (91), and gas chromatographic retention times (92).…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

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Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

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“…The SVM is another ML algorithm used in support vector classification to find a hyperplane in both classification and regression analyses [86][87][88]. This algorithm has been applied in regression analysis for the prediction of biological activity against P. falciparum.…”

Section: Svm Algorithmmentioning

confidence: 99%

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

et al. 2022

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Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum . Some major challenges include the dearth of suitable animal models for anti- P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium . Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC 50 values of bioactive compounds against P. falciparum . The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

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Prediction of Depuration Rate Constants for Polychlorinated Biphenyl Congeners

Cited by 8 publications

References 27 publications

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

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