Modelling the Correlation Between Molecular Electrostatic Potential and pKa on Sets of Carboxylic Acids, Phenols and Anilines

Virant, Miha; Drvarič-Talian, Sara; Podlipnik, Črtomir; Hribar-Lee, Barbara

doi:10.17344/acsi.2016.2962

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…The second property we investigate is acidity of functional groups, which can affect key medicinal chemistry parameters like solubility and affinity. It was shown that p K a values of acidic groups correlate with electrostatic potentials, either at the hydrogen atom of the acidic group or at the lone pair of the conjugate base. − Here, we used data for a number of 4-substituted pyridines and aliphatic carboxylic acids (see Supporting Information), extracted from a p K a database, to assess whether we see a similar correlation for ESP values generated using our DNN-fp model. Figure B shows that there is a clear correlation between the lone pair ESP value of the (conjugate) base and the corresponding p K a value for both the pyridines and carboxylic acids.…”

Section: Resultsmentioning

confidence: 99%

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Rathi¹,

Ludlow²,

Verdonk³

2019

J. Med. Chem.

100

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Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typically means time-consuming high-level quantum mechanics (QM) calculations are required. For interactive design much faster alternative methods are required. Here, we present a graph convolutional deep neural network (DNN) model, trained on ESP surfaces derived from high quality QM calculations, that generates ESP surfaces for ligands in a fraction of a second. Additionally, we describe a method for constructing fast QM-trained ESP surfaces for proteins. We show that the DNN model generates ESP surfaces that are in good agreement with QM and that the ESP values correlate well with experimental properties relevant to medicinal chemistry. We believe that these high-quality, interactive ESP surfaces form a powerful tool for driving drug discovery programs forward. The trained model and associated code are available from https:// github.com/AstexUK/ESP_DNN.

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Section: Resultsmentioning

confidence: 99%

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Rathi¹,

Ludlow²,

Verdonk³

2019

J. Med. Chem.

100

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“…Other efforts include correlations on the excited states of photoacids [14] using Time Dependent DFT at the ωB97X-D/6-31G( d ) level of theory for a family of hydroxyl-substituted aromatic compounds. QSPR models have yielded, for instance, a three parameters model which uses the MEP maxima, the number of carboxylic acid and amine groups for phenols, at the HF/6-31G( d , p ) and B3LYP/6-31G( d , p ) levels of theory ( R 2 = 0.96) [15]. It involves a four parameters linear equation comprising the highest normal mode vibrational frequency, the partial positive and negative charges divided by the total surface area and a reactivity index, defined in terms of a population analysis on the frontier orbital HOMO ( R 2 ca.…”

Section: Introductionmentioning

confidence: 99%

Calculation of VS,max and Its Use as a Descriptor for the Theoretical Calculation of pKa Values for Carboxylic Acids

et al. 2018

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The theoretical calculation of pKa values for Brønsted acids is a challenging task that involves sophisticated and time-consuming methods. Therefore, heuristic approaches are efficient and appealing methodologies to approximate these values. Herein, we used the maximum surface electrostatic potential (VS,max) on the acidic hydrogen atoms of carboxylic acids to describe the H-bond interaction with water (the same descriptor that is used to characterize σ-bonded complexes) and correlate the results with experimental pKa values to obtain a predictive model for other carboxylic acids. We benchmarked six different methods, all including an implicit solvation model (water): Five density functionals and the Møller–Plesset second order perturbation theory in combination with six different basis sets for a total of thirty-six levels of theory. The ωB97X-D/cc-pVDZ level of theory stood out as the best one for consistently reproducing the reported pKa values, with a predictive power of 98% correlation in a test set of ten other carboxylic acids.

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“…In the case of catalysis with the Lewis acid BF 3 •Et 2 O, the pK a value of ArOH-BF 3 was calculated, applying an earlier reported relation between the maximum electrostatic potential (MEP) at the isodensity surface: 0.002 e/au 3 and pK a [19]. By taking structurally similar sulfonic acids with experimentally known pK a s, its accuracy was further increased.…”

mentioning

confidence: 99%

A Molecular Modeling Study into Brønsted and Lewis Acid Catalyzed Conversion of CBD into Other Cannabinoids

Buijs

2024

Biologics

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There is a continuous interest in cannabinoids like Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD). Previous experimental research has described the conversion of CBD to either Δ8-THC or Δ9-THC, depending on the acid catalyst applied. The use of para-toluene sulfonic acid (pTSA) has led to the formation of Δ8-THC, while boron trifluoride etherate (BF3·Et2O) has mainly yielded Δ9-THC. The enormous difference in product selectivity between these two catalysts was investigated with Molecular Modeling, applying quantum chemical density functional theory. It was found that pTSA leads to fast isomerization of Δ9-CBD to Δ8-CBD and subsequent ring closure to Δ8-THC. BF3·Et2O catalysis leads to the formation of tertiary carbenium ions in the transition states, which yield Δ9-THC and some iso THC. Under dry conditions in refluxing toluene, it was found that pTSA is predominantly present as a dimer, and only a small fraction is available as monomeric catalyst. Applying the computationally derived activation barriers in transition state theory yielded reaction rates that predicted the amounts of cannabinoids that are in close agreement with the experimental findings from the previous literature.

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Modelling the Correlation Between Molecular Electrostatic Potential and pKa on Sets of Carboxylic Acids, Phenols and Anilines

Cited by 9 publications

References 11 publications

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Calculation of VS,max and Its Use as a Descriptor for the Theoretical Calculation of pKa Values for Carboxylic Acids

A Molecular Modeling Study into Brønsted and Lewis Acid Catalyzed Conversion of CBD into Other Cannabinoids

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