The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

Ortiz, Erlinda V.; Bennardi, Daniel Oscar; Bacelo, Daniel E.; Fioressi, Silvina E.; Duchowicz, Pablo R.

doi:10.1007/s11356-017-0315-5

Cited by 22 publications

(11 citation statements)

References 39 publications

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“…Only a handful of prediction trials were reported using a limited number of variations of reaction conditions. [1,2] On the other hand, reaction rate constants, [3][4][5][6] equilibrium constants [7] and enantioselectivity, [8][9][10] which are associated with energy differences, have been the main targets for predictive models. These parameters can be predicted from the independent variables which are numerically representable and are assumed to be important for the target reaction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning

Sato

Miyao

Funatsu

2021

Molecular Informatics

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Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on selfattention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations.

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Section: Introductionmentioning

confidence: 99%

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning

Sato

Miyao

Funatsu

2021

Molecular Informatics

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“…The log K values predicted with optimal SVM model are listed in Table and sketched in Figure 1. The training and test sets have coefficients of determination R 2 of 0.923 and 0.925, respectively, which are higher than those ( R 2 = 0.584–0.918) in the models (Borhani et al, 2016; Gupta & Basant, 2017; Jin et al, 2015; Luo et al, 2017; Ortiz et al, 2017; Sudhakaran & Amy, 2013; Toropov et al, 2012; Wols & Vries, 2012), although these models dealt with relatively small data sets of the reaction rate constants log k OH between •OH and organic compounds in aqueous phase.…”

Section: Resultsmentioning

confidence: 85%

“…Recently, Ortiz et al (2017) employed five descriptors to obtain a MLR model for log k OH of 118 organic compounds. The MLR model was obtained from a training set (89 compounds) and validated with a test set (29 compounds).…”

Section: Introductionmentioning

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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“…They were catalogued from the Supporting Information from ref. Zhong et al, Borhani et al, Ortiz et al, Xiao et al, Wojnárovits and Takács the IscoKin, and NIST database. The kinetic parameters are catalogued under standard conditions, 25 °C and 1 mol·L –1 in the aqueous phase.…”

Section: Methodsmentioning

confidence: 99%

“pySiRC”: Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

Sanches-Neto

Dias-Silva

Queiroz

et al. 2021

Environ. Sci. Technol.

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We developed a web application structured in a machine learning and molecular fingerprint algorithm for the automatic calculation of the reaction rate constant of the oxidative processes of organic pollutants by • OH and SO 4•− radicals in the aqueous phasethe pySiRC platform. The model development followed the OECD principles: internal and external validation, applicability domain, and mechanistic interpretation. Three machine learning algorithms combined with molecular fingerprints were evaluated, and all the models resulted in high goodness-of-fit for the training set with R 2 > 0.931 for the • OH radical and R 2 > 0.916 for the SO 4•− radical and good predictive capacity for the test set with R ext 2 = Q ext 2 values in the range of 0.639−0.823 and 0.767−0.824 for the • OH and SO 4•− radicals. The model was interpreted using the SHAP (SHapley Additive exPlanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing and -donating groups interfere with the reactivity of the • OH and SO 4•− radicals. We hope that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water treatment units based on advanced oxidative technologies.

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The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

Cited by 22 publications

References 39 publications

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning

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

“pySiRC”: Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

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