A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (S<sub>N</sub>Ar) using simple descriptors

Paci, Irina; Leitch, David C.

doi:10.1039/d2sc04041g

Cited by 24 publications

(21 citation statements)

References 135 publications

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“…For aryl nuclei containing electron‐withdrawing groups, including sulphonyl, polyhalogens, nitro, cyano, and trifluoromethyl groups, studies on the S N Ar of fluorine by various nucleophilic substituents have been reported. [ 18,19 ] Using these electron‐withdrawing groups increases the speed of the S N Ar reaction process and produces excellent yields. Solvents, including hexamethylphosphoramide, dimethyl sulfoxide, dimethylacetamide, and dimethylformamide, are often used for S N Ar reactions because of their polarity and aprotic nature.…”

Section: Introductionmentioning

confidence: 99%

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

Al-Anazi

2023

Luminescence

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Nucleophilic aromatic substitution (SNAr) chemistry has been applied to develop many functionalized pentafluorobenzene derivatives. Those compounds are highly specific at the para position of the fluorinated ring. Therefore, they are typical adducts for the preparation of antioxidant molecular systems. In this context, we report the use of SNAr chemistry as a suitable and simple approach for the synthesis of fluorescent antioxidant perfluorinated materials bearing ether bonds in various para‐substituted alkoxy chains and with high purity and excellent yields. The fluoroterphenyl core was prepared via alkylation, Cu(I)‐assisted decarboxylation, and cross‐coupling using the potassium salt of fluorobenzoate, followed by the reaction with different alcohols. The structures of the synthesized fluoroterphenyl adducts were investigated using FT‐IR, 1H NMR, 13C NMR, and 19F NMR spectroscopy. The emission spectra and absorption spectra showed solvatochromism. The newly prepared tetrafluoroterphenyl analogues were investigated by antioxidant examination using the 2,2‐diphenyl‐1‐picrylhydrazyl assay. Results were compared with ascorbic acid and butylated hydroxytoluene as references, and revealed that the tetrafluoroterphenyl analogues containing a decyl chain had the highest activity, with an IC50 value of 22.36 ± 0.19 g/ml. The produced tetrafluoroterphenyl analogues were used in molecular docking strategies with a Protein Data Bank protein ID 5IKQ. The antioxidant investigations and docking results were convergent.

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Section: Introductionmentioning

confidence: 99%

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

Al-Anazi

2023

Luminescence

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“…Tested on Pfizer’s in-house dataset and USPTO public data, the complete workflow achieves around 95% accuracy on both datasets. The model developed by Jorner et al achieved an accuracy of 86% under the same metrics, and another recent linear regression model by Lu and co-workers reached 91% accuracy. While direct comparison is difficult since they were tested on different data sets, our workflow outperforms the two cited work mainly because of two points.…”

Section: Discussionmentioning

confidence: 78%

“…Moreover, confident predictions dominate the first-step ML predictions. Compared to the reported models, , only a minor portion of the reactions will trigger DFT modeling (compared to the conventional TST modeling which computes all reacting pairs, the proposed workflow only takes 23 and 36% of computing resources, estimated on Pfizer and USPTO datasets, respectively), which allows us to screen more candidates or save computational resources for other tasks.…”

Section: Discussionmentioning

confidence: 99%

S_NAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold

Guan

Lee

Wang

et al. 2023

J. Chem. Inf. Model.

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Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either timeconsuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for S N Ar reactions. Our method is accurate and fast, which achieves 96.3 and 94.7% accuracy in predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time.

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“…Moreover, confident predictions dominate the first-step machine learning predictions. Compared to reported models 13,63 , only a minor portion of the reactions will trigger DFT modeling, which allows us to screen more candidates or save computational resources for other tasks.…”

Section: Discussionmentioning

confidence: 99%

SNAr Regioselectivity Predictions: Machine Learning Trigger-ing DFT Reaction Modeling through Statistical Threshold

Guan

Lee

Wang

et al. 2022

Preprint

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Fast and accurate prospective predictions of the regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods have been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data dependent. Herein, we introduce a prototype seamlessly bridging machine learning and TST modeling by triggering the resource-intensive but much less domain sensitive DFT calculation only on less confident ML predictions. The proposed workflow was trained and tested on both Pfizer internal dataset and USPTO public dataset to predict regioselectivity for SNAr reactions. Our method is accurate and fast which achieves 96.3% and 94.7% accuracy predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time.

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A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (S_NAr) using simple descriptors

Abstract: We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (SNAr) reactions based on the electrophile structure. This...

Cited by 24 publications

References 135 publications

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

S_NAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold

SNAr Regioselectivity Predictions: Machine Learning Trigger-ing DFT Reaction Modeling through Statistical Threshold

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A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

Abstract: We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (SNAr) reactions based on the electrophile structure. This...

Cited by 24 publications

References 135 publications

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

Synthesis, molecular docking, and antioxidant activity of new fluorescent tetrafluoroterphenyl analogues

SNAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold

SNAr Regioselectivity Predictions: Machine Learning Trigger-ing DFT Reaction Modeling through Statistical Threshold

Contact Info

Product

Resources

About

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (S_NAr) using simple descriptors

S_NAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold