Correlation between 13C NMR chemical shifts and complete sets of descriptors of natural coumarin derivatives

Yu, Xinliang; Lisha, XU; Zhu, Yali; Lu, Shuo; Dang, Limin

doi:10.1016/j.chemolab.2018.12.006

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…The reason is that they have dissimilar molecular structures with other chemicals in the training set (Keivanimehr et al, 2020; Yu, 2020a, 2020b). Furthermore, Figure 2 shows that there are 76 organic compounds with leverages h > h * of 0.03 (here h * is warning leverage and calculated with h * = 3 × ( p + 1)/ n , p, and n are, respectively, the numbers of descriptors and compounds in training set) (Liao et al, 2019; Liu, Bai, et al, 2020; Liu, Deng, et al, 2020; Yu et al, 2019; Yu et al, 2019). But their | σ | values are less than 3.…”

Section: Resultsmentioning

confidence: 99%

Prediction of reaction rate constants of hydroxyl radical with chemicals in water

2020

Water Environment Research

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The rate constants (kOH) of the reactions between organic micropollutants with hydroxyl radical (•OH) in aqueous systems are an important parameter to evaluate the persistence of organic compounds in the environment. In this paper, a support vector machine (SVM) model based on five descriptors was built to predict the reaction rate constants (log K = (log kOH)/MW). The quantitative structure–activity relationship (QSAR) model of log K was obtained from a training set (600 compounds) and validated with a test set (395 compounds). The coefficients of determination R2 of the training and test sets are 0.923 and 0.925, respectively. The results suggest that the SVM model developed in this work possesses satisfactory prediction ability. Practitioner points The rate constants of the reactions of organic micropollutants with •OH in aqueous systems were investigated. SVM model was established for the reaction rate constants (log K = (log kOH)/MW). Only five molecular descriptors were used to predict 995 log K. A large data set was used for the test set (395 compounds).

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Section: Resultsmentioning

confidence: 99%

Prediction of reaction rate constants of hydroxyl radical with chemicals in water

2020

Water Environment Research

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“…The main idea of the SVM used in the regression prediction is to map the input characteristic parameters into a high-dimensional feature space through a nonlinear mapping function and then carry out a linear regression analysis. The SVM algorithm is based on the following regression 2,4,24 where n is the number of samples used in the training set, φ( x ) is the mapping function, x is the input vector composed of characteristic parameters reflecting molecular structure, and f ( x ) is the output of prediction results. The coefficients w and b can be estimated by means of the following minimizationsubject towhere C is a penalty parameter used for adjusting the training error.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Depuration Rate Constants for Polychlorinated Biphenyl Congeners

2019

ACS Omega

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The integral equation formalism polarizable continuum model (IEF-PCM) for solvent effects with the default solvent (water) and solvent parameters, together with the density functional theory method at 6-31G(d) level, was used to optimize molecular structures for polychlorinated biphenyl (PCB) congeners. Four molecular descriptors were selected to develop quantitative structure–activity relationship (QSAR) models for the depuration rate constants (kd) of 63 PCB congeners in a juvenile rainbow trout (Oncorhynchus mykiss). The optimal multiple linear regression (MLR) model has the correlation coefficient R of 0.933 and the root mean square (rms) error of 0.0681 for the total set of 63 PCB congeners. The support vector regression model has R of 0.953 and rms error of 0.0576 for the total set. Both the MLR and SVM QSAR models in this paper were accurate and acceptable compared with other QSAR models for the depuration rate of PCB congeners reported in references. Thus, applying IEF-PCM and B3LYP/6-31G(d) calculations for molecular descriptor derivation of PCB congeners is successful.

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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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Correlation between 13C NMR chemical shifts and complete sets of descriptors of natural coumarin derivatives

Cited by 11 publications

References 30 publications

Prediction of reaction rate constants of hydroxyl radical with chemicals in water

Prediction of reaction rate constants of hydroxyl radical with chemicals in water

Prediction of Depuration Rate Constants for Polychlorinated Biphenyl Congeners

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

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