pKa Prediction from “Quantum Chemical Topology” Descriptors

Exploring enzyme inhibition and comprehensive mechanisms of antioxidant/prooxidative activity of natural furanocoumarin derivatives: A comparative kinetic DFT study

This study aimed to explore the antioxidant and prooxidative activity of two natural furanocoumarin derivatives, Bergaptol (4-Hydroxy-7H-furo [3,2-g] [1]benzopyran-7-one, BER) and Xanthotoxol (9-Hydroxy-7H-furo [3,2-g] [1]benzopyran-7-one, XAN). The collected thermodynamic and kinetic data demonstrate that both compounds possess substantial antiradical activity against HO• and CCl3OO• radicals in physiological conditions. BER exhibited better antiradical activity in comparison to XAN, which can be attributed to the enhanced deprotonation caused by the positioning of the -OH group on the psoralen ring. In contrast to highly reactive radical species, newly formed radical species BER• and XAN• exhibited negligible reactivity towards the chosen constitutive elements of macromolecules (fatty acids, amino acids, nucleobases). Furthermore, in the presence of O2•─, the ability to regenerate newly formed radicals BER• and XAN• was observed. Conversely, in physiological conditions in the presence of Cu(II) ions, both compounds exhibit prooxidative activity. Nevertheless, the prooxidative activity of both compounds is less prominent than their antioxidant activity. Furthermore, it has been demonstrated that anionic species can engage in the creation of a chelate complex, which restricts the reduction of metal ions when reducing agents are present (O2•─ and Asc─). Moreover, studies have demonstrated that these chelating complexes can be coupled with other radical species, hence enhancing their ability to inactivate radicals. Both compounds exhibited substantial inhibitory effects against enzymes involved in the direct or indirect generation of ROS: Xanthine Oxidase (XOD), Lipoxygenase (LOX), Myeloperoxidase (MPO), NADPH oxidase (NOX).

Explainable Graph Neural Networks with Data Augmentation for Predicting pKa of C-H Acids

The pKa of C-H acids is an important parameter in the fields of organic synthesis, drug discovery, and materials science. However, the prediction of pKa is still a great challenge due to the limit of experimental data and the lack of chemical insight. Here, a new model for predicting the pKa values of C-H acids is proposed on the basis of graph neural networks (GNNs) and data augmentation. A message passing unit (MPU) was used to extract the topological and target-related information from the molecular graph data, and a readout layer was utilized to retrieve the information on the ionization site C atom. The retrieved information then was adopted to predict pKa by a fully connected network. Furthermore, to increase the diversity of the training data, a knowledge-infused data augmentation technique was established by replacing the H atoms in a molecule with substituents exhibiting different electronic effects. The MPU was pretrained with the augmented data. The efficacy of data augmentation was confirmed by visualizing the distribution of compounds with different substituents and by classifying compounds. The explainability of the model was studied by examining the change of pKa values when a specific atom was masked. This explainability was used to identify the key substituents for pKa. The model was evaluated on two data sets from the iBonD database. Dataset1 includes the experimental pKa values of C-H acids measured in DMSO, while dataset2 comprises the pKa values measured in water. The results show that the knowledge-infused data augmentation technique greatly improves the predictive accuracy of the model, especially when the number of samples is small.

Screening the possible anti-cancer constituents of Hibiscus rosa-sinensis flower to address mammalian target of rapamycin: an in silico molecular docking, HYDE scoring, dynamic studies, and pharmacokinetic prediction

One of the most common malignancies diagnosed and the leading cause of death for cancer-stricken women globally is breast cancer. The molecular subtype affects therapy options because it is a complex disorder with multiple subtypes. By concentrating on receptor activation, mTOR (mammalian target of rapamycin) can be employed as a therapeutic target. The goal of this work was to screen a number of inhibitors produced from Hibiscus rosa-sinensis for possible target to inhibit the mTOR and to determine which has the greatest affinity for the receptor. Primarily, the ionization states of the chosen compounds were predicted using the ChemAxon web platform, and their pKa values were estimated. Given the significance of interactions between proteins in the development of drugs, structure-based virtual screening was done using AutoDock Vina. Approximately 120 Hibiscus components and ten approved anti-cancer drugs, including the mTOR inhibitor everolimus, were used in the comparative analysis. By using Lipinski's rule of five to the chosen compounds, the ADMET profile and drug-likeness characteristics were further examined to assess the anti-breast cancer activity. The compounds with the highest ranked binding poses were loaded using the SeeSAR tool and the HYDE scoring to give interactive, desolvation, and visual ΔG estimation for ligand binding affinity assessment. Following, the prospective candidates underwent three replicas of 100 ns long molecular dynamics simulations, preceded with MM-GBSA binding free energy calculation. The stability of the protein-ligand complex was determined using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions. The results demonstrated that the best mTOR binding affinities were found for stigmastadienol (107), lupeol (66), and taraxasterol acetate (111), which all performed well in comparison to the control compounds. Thus, bioactive compounds isolated from Hibiscus rosa-sinensis could serve as lead molecules for the creation of potent and effective mTOR inhibitors for the breast cancer therapy.

Discovery of potential mTOR inhibitors from Cichorium intybus to find new candidate drugs targeting the pathological protein related to the breast cancer: an integrated computational approach

Breast cancer is the most common malignancy among women. It is a complex condition with many subtypes based on the hormone receptor. The mammalian target of the rapamycin (mTOR) pathway regulates cell survival, metabolism, growth, and protein synthesis in response to upstream signals in both normal physiological and pathological situations, primarily in cancer. The objective of this study was to screen for a potential target to inhibit the mTOR using a variety of inhibitors derived from Cichorium intybus and to identify the one with the highest binding affinity for the receptor protein. Initially, AutoDock Vina was used to perform structure-based virtual screening, as protein-like interactions are critical in drug development. For the comparative analysis, 110 components of Cichorium intybus were employed and ten FDA-approved anticancer medicines, including everolimus, an mTOR inhibitor. Further, the drug-likeness and ADMET properties were investigated to evaluate the anti-breast cancer activity by applying Lipinski's rule of five to the selected molecules. The promising candidates were then subjected to three replica molecular dynamics simulations run for 100 ns, followed by binding free energy estimation using MM-GBSA. The data were analyzed using root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and protein-ligand interactions to determine the stability of the protein-ligand complex. Based on the results, taraxerone (98) revealed optimum binding affinities with mTOR, followed by stigmasterol (110) and rutin (104), which compared favorably to the control compounds. Subsequently, bioactive compounds derived from Cichorium intybus may serve as lead molecules for developing potent and effective mTOR inhibitors to treat breast cancer.

Quantum Mechanical Prediction of Dissociation Constants for Thiazol-2-imine Derivatives

As weak acids or bases, in solution, drug molecules are in either their ionized or nonionized states. A high degree of ionization is essential for good water solubility of a drug molecule and is required for drug-receptor interactions, whereas the nonionized form improves a drug's lipophilicity, allowing the ligand to cross the cell membrane. The penetration of a drug ligand through cell membranes is mainly governed by the pKa of the drug molecule and the membrane environment. In this study, with the aim of predicting the acetonitrile pKa's (pKa(MeCN)) of eight drug-like thiazol-2-imine derivatives, we propose a very accurate and computationally affordable protocol by using several quantum mechanical approaches. Benchmark studies were conducted on a set of training molecules, which were selected from the literature with known pKa(water) and pKa(MeCN). Highly well-correlated pKa values were obtained when the calculations were performed with the isodesmic method at the M062X/6-31G** level of theory in conjunction with SMD solvation model for nitrogen-containing heterocycles. Finally, experimentally unknown pKa(MeCN) values of eight thiazol-2-imine structures, which were previously synthesized by some of us, are proposed.

Machine learning methods for pKa prediction of small molecules: Advances and challenges

The acid-base dissociation constant (pK_a) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pK_a prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pK_a prediction, including descriptor-based and graph-based approaches, and summarize their pros and cons. Moreover, we highlight the current challenges and future directions regarding experimental data, crucial factors influencing pK_a and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.

Palladium-Catalyzed Cross-Coupling Reaction of Diarylmethanol Derivatives with Diborylmethane

A palladium-catalyzed cross-coupling reaction of diarylmethanol derivatives with diborylmethane has been developed. The reaction proceeds chemoselectively to deliver the corresponding homobenzylic boronates in good yields.

Kernel Methods for Predicting Yields of Chemical Reactions

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.

Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices

This study shows that the model fitting based on machine learning (ML) from experimental data can successfully predict the electrochromic characteristics of single- and dual-type flexible electrochromic devices (ECDs) by using tungsten trioxide (WO3) and WO3/vanadium pentoxide (V2O5), respectively. Seven different regression methods were used for experimental observations, which belong to single and dual ECDs where 80% percent was used as training data and the remaining was taken as testing data. Among the seven different regression methods, K-nearest neighbor (KNN) achieves the best results with higher coefficient of determination (R 2) score and lower root-mean-squared error (RMSE) for the bleaching state of ECDs. Furthermore, higher R 2 score and lower RMSE for the coloration state of ECDs were achieved with Gaussian process regressor. The robustness result of the ML modeling demonstrates the reliability of prediction outcomes. These results can be proposed as promising models for different energy-saving flexible electronic systems.

Aqueous pKa prediction for tautomerizable compounds using equilibrium bond lengths

The accurate prediction of aqueous pK_a values for tautomerizable compounds is a formidable task, even for the most established in silico tools. Empirical approaches often fall short due to a lack of pre-existing knowledge of dominant tautomeric forms. In a rigorous first-principles approach, calculations for low-energy tautomers must be performed in protonated and deprotonated forms, often both in gas and solvent phases, thus representing a significant computational task. Here we report an alternative approach, predicting pK_a values for herbicide/therapeutic derivatives of 1,3-cyclohexanedione and 1,3-cyclopentanedione to within just 0.24 units. A model, using a single ab initio bond length from one protonation state, is as accurate as other more complex regression approaches using more input features, and outperforms the program Marvin. Our approach can be used for other tautomerizable species, to predict trends across congeneric series and to correct experimental pK_a values.

Terminal Ligand (L) Effects on Zero-Magnetic-Field Splitting in the Excited Triplet States of [{Mo6Br8}L6]2- (L = Aromatic Carboxylates)

We report the emission properties of the octahedral hexamolybdenum(II) bromide-core ({Mo₆Br₈}⁴⁺) clusters having a series of terminal aromatic carboxylate ligands (RCOO), [{Mo₆Br₈}(RCOO)₆]^2-, in solution and crystalline phases. The acid dissociation constant of RCOOH (p K_a(L)) was shown to govern the redox and emission properties of the clusters. Temperature ( T)-controlled emission experiments (3-300 K) demonstrated that the clusters showed large T-dependent emission energies (ν̃^em) and lifetimes (τ^em) because of zero-magnetic-field splitting in the emissive excited triplet (T₁) states. The spin sublevel (Φ _n, n = 1-4) model in the T₁ state of the cluster explained very well the T-dependent emission characteristics (ν̃^em and τ^em), irrespective of the clusters studied. Furthermore, we revealed that the energy difference between the lowest-energy (Φ₁) and energetically upper-lying third (Φ₃) or fourth spin sublevels (Φ₄), Δ E₁₃ or Δ E₁₄, respectively, correlated very well with p K_a( L). The results are discussed in terms of the variation of the effective nuclear charge of the Mo metal center(s) in [{Mo₆Br₈}(RCOO)₆]^2- with that of p K_a(L).

Sequential halochromic/mechanochromic luminescence of pyridyl-substituted solid-state emissive dyes: thermally controlled stepwise recovery of the original emission color

A stepwise temperature-controlled emission-color switch has been achieved in a system that combines halochromic and mechanochromic luminescence in series.

Using the Relative Energy Gradient Method with Interacting Quantum Atoms to Determine the Reaction Mechanism and Catalytic Effects in the Peptide Hydrolysis in HIV-1 Protease

The reaction mechanism in an active site is of the utmost importance when trying to understand the role that an enzyme plays in biological processes. In a recently published paper [Theor. Chem. Acc. 2017, 136, 86], we formalised the Relative Energy Gradient (REG) method for automating an Interacting Quantum Atoms (IQA) analysis. Here, the REG method is utilised to determine the mechanism of peptide hydrolysis in the aspartic active site of the enzyme HIV-1 Protease. Using the REG method along with the IQA approach we determine the mechanism of peptide hydrolysis without employing any arbitrary parameters and with remarkable ease (albeit at large computational cost: the system contains 133 atoms, which means that there are 17 689 individual IQA terms to be calculated). When REG and IQA work together it is possible to determine a reaction mechanism at atomistic resolution from data directly derived from quantum calculations, without arbitrary parameters. Moreover, the mechanism determined by this novel method gives concrete insight into how the active site residues catalyse peptide hydrolysis.

Interacting quantum fragments-rooted preorganized-interacting fragments attributed relative molecular stability of the Be(II) complexes of nitrilotriacetic acid and nitrilotri-3-propionic acid

A method designed to investigate, on a fundamental level, the origin of relative stability of molecular systems using Be(II) complexes with nitrilotriacetic acid (NTA) and nitrilotri-3-propionic acid (NTPA) is described. It makes use of the primary and molecular fragment energy terms as defined in the IQA/F (Interacting Quantum Atoms/Fragments) framework. An extensive classical-type investigation, focused on single descriptors (bond length, density at critical point, the size of metal ion or coordination ring, interaction energy between Be(II) and a donor atom, etc.) showed that it is not possible to explain the experimental trend. The proposed methodology is fundamentally different in that it accounts for the total energy contributions coming from all atoms of selected molecular fragments, and monitors changes in defined energy terms (e.g., fragment deformation, inter- and intra-fragment interaction) on complex formation. By decomposing combined energy terms we identified the origin of relative stability of Be(II) (NTA) and Be(II) (NTPA) complexes. We found that the sum of coordination bonds' strength, as measured by interaction energies between Be(II) ion and donor atoms, favours Be(II) (NTA) but the binding energy of Be(II) ion to the entire ligand correlates well with experimental trend. Surprisingly, the origin of Be(II) (NTPA) being more stable is due to less severe repulsive interactions with the backbone of NTPA (C and H-atoms). This general purpose protocol can be employed not only to investigate the origin of relative stability of any molecular system (e.g., metal complexes) but, in principle, can be used as a predictive tool for, e.g., explaining reaction mechanism. © 2016 Wiley Periodicals, Inc.

Linear free-energy relationships between a single gas-phase ab initio equilibrium bond length and experimental pKa values in aqueous solution

Remarkably simple yet effective linear free energy relationships were discovered between a single ab initio computed bond length in the gas phase and experimental pKa values in aqueous solution. The formation of these relationships is driven by chemical features such as functional groups, meta/para substitution and tautomerism. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets (HCSs). Surprisingly, all molecules in a given high correlation subset share the same conformation in the gas phase. Here we show that accurate pKa values can be predicted from such HCSs. This is achieved within an accuracy of 0.2 pKa units for 5 drug molecules.

Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pK(a)'s, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme-substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as "interacting quantum atoms (IQA)" energies which are expressible into an interaction matrix of two body terms (and diagonal one body "self" terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established.

A quantum chemical topological analysis of the C-C bond formation in organic reactions involving cationic species

ELF topological analysis of the ionic Diels-Alder (I-DA) reaction between the N,N-dimethyliminium cation and cyclopentadiene (Cp) has been performed in order to characterise the C-C single bond formation. The C-C bond formation begins in the short range of 2.00-1.96 Åvia a C-to-C pseudoradical coupling between the most electrophilic center of the iminium cation and one of the two most nucleophilic centers of Cp. The electron density of the pseudoradical center generated at the most electrophilic carbon of the iminium cation comes mainly from the global charge transfer which takes place along the reaction. Analysis of the global reactivity indices indicates that the very high electrophilic character of the iminium cation is responsible for the negative activation energy found in the gas phase. On the other hand, the analysis of the radical P(k)(o) Parr functions of the iminium cation, and the nucleophilic P(k)(-) Parr functions of Cp makes the characterisation of the most favourable two-center interaction along the formation of the C-C single bond possible.

Machine learning methods in chemoinformatics

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.

A new C–C bond formation model based on the quantum chemical topology of electron density

Pseudodiradical structures and GEDT involved in the C–C single bond formation in non-polar, polar and ionic organic reactions.

MOLECULAR ACIDITY OF BUILDING BLOCKS OF BIOLOGICAL SYSTEMS: A DENSITY FUNCTIONAL REACTIVITY THEORY STUDY

An accurate prediction of the molecular acidity by employing ab initio or density functional approaches for typical molecular systems is still challenging. Recently, we proposed to utilize two quantum descriptors, molecular electrostatic potential (MEP) and the sum of valence natural atomic orbital (NAO) energies on the nucleus of both the acidic atom and leaving proton, to quantitatively evaluate the pKa values. This new approach has been validated by a number of organic and inorganic systems and justified within the framework of density functional reactivity theory (DFRT). In this work, we apply the approach to building blocks of biological systems, namely, 20 natural α-amino acids and 5 DNA/RNA bases, together with a few other biologically relevant species. Our results show that there exists a strong linear correlation between MEP on the nucleus of the N atom and the sum of N 2p NAO energies, with the correlation coefficient R2 = 0.99. Also, we observe that both MEP on the nitrogen nucleus and the sum of N 2p NAO energies correlate well with experimental pKa values, with the correlation coefficient equal to 0.91. Using this established model, we predicted the trend of pKa changes of amino acids in proteins with different dielectric constants. We also applied the model to predict pKa values for dipeptides. Implications of these linear relationships to understand functions and reactivity of biological systems are discussed as well.

Predicting pKa Values in Aqueous Solution for the Guanidine Functional Group from Gas Phase Ab Initio Bond Lengths

Here we applied a novel method1a to predict pKa values of the guanidine functional group, which is a notoriously difficult. This method, which was developed in our lab, uses only one ab initio bond length obtained at a low level of theory. The method is shown to work for drug molecules, delivers prediction errors of less than 0.5 log units, successfully treats tautomerisation in close relation with experiment, and demonstrates strong correlations with only a few data points. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets. One then observes that molecules within a subset possess a common substructure. Each high correlation subset exists in its own region of chemical space. The high correlation subset method is explored with respect to this position in chemical space, in particular tautomerisation. The proposed method is able to distinguish between different tautomeric forms and the preferred tautomeric form emerges naturally, in agreement with experiment.

pKa prediction from an ab initio bond length: part 2--phenols

The prediction of pK(a) continues to attract much attention with ongoing investigations into new ways to predict pK(a) accurately, where predicted pK(a) values deviate less than 0.50 log units from experiment. We show that a single descriptor, i.e. an ab initio bond length, can predict pK(a). The emphasis was placed on model simplicity and a demonstration that more accurate predictions emerge from single-bond-length models. A data set of 171 phenols was studied. The carbon-oxygen bond length, connecting the OH to the phenyl ring, consistently provided accurate predictions. The pK(a) of meta- and para-substituted phenols is predicted here by a single-bond-length model within 0.50 log units. However, accurate prediction of the pK(a) of ortho-substituted phenols necessitated their splitting into groups called high-correlation subsets in which the pK(a) of the compounds strongly correlated with a single bond-length. The highly compound-specific single-bond-length models produced better predictions than models constructed with more compounds and more bond lengths. Outliers were easily identified using single-bond-length models and in most cases we were able to determine the reason for the outlier discrepancy. Furthermore, the single-bond-length models showed better cross-validation statistics than the PLS models constructed using more than one bond length. For all of the single-bond-length models, RMSEE was less than 0.50. For the majority of the models, RMSEP was less than 0.50. The results support the use of multiple high-correlation subsets and a single bond-length to predict pK(a). Six one-term linear equations are listed as a starting point for the construction of a more comprehensive list covering a larger variety of compound classes.

pKa prediction from an ab initio bond length: part 3--benzoic acids and anilines

The prediction of pK(a) from a single ab initio bond length has been extended to provide equations for benzoic acids and anilines. The HF/6-31G(d) level of theory is used for all geometry optimisations. Similarly to phenols (Part 2 of this series of publications), the meta-/para-substituted benzoic acids can be predicted from a single model constructed from one bond length. This model had an impressive RMSEP of 0.13 pK(a) units. The prediction of ortho-substituted benzoic acids required the identification of high-correlation subsets, where the compounds in the same subset have at least one of the same (e.g. halogens, hydroxy) ortho substituent. Two pK(a) equations are provided for o-halogen benzoic acids and o-hydroxybenzoic acids, where the RMSEP values are 0.19 and 0.15 pK(a) units, respectively. Interestingly, the bond length that provided the best model differed between these two high-correlation subsets. This demonstrates the importance of investigating the most predictive bond length, which is not necessarily the bond involving the acid hydrogen. Three high-correlation subsets were identified for the ortho-substituted anilines. These were o-halogen, o-nitro and o-alkyl-substituted aniline high-correlation subsets, where the RMSEP ranged from 0.23 to 0.44 pK(a) units. The RMSEP for the meta-/para-substituted aniline model was 0.54 pK(a) units. This value exceeded our threshold of 0.50 pK(a) units and was higher than both the m-/p-benzoic acids in this work and the m-/p-phenols (RMSEP = 0.43) of Part 2. Constructing two separate models for the meta- and para- substituted anilines, where RMSEP values of 0.63 and 0.33 pK(a) units were obtained respectively, revealed it was the meta-substituted anilines that caused the large RMSEP value. For unknown reasons the RMSEP value increased with the addition of a further twenty meta-substituted anilines to this model. The C-N bond always produced the best correlations with pK(a) for all the high-correlation subsets. A higher level of theory and an ammonia probe improved the statistics only marginally for the hydroxybenzoic acid high-correlation subsets.

Fate of organic micropollutants in the hyporheic zone of a eutrophic lowland stream: results of a preliminary field study

Many rivers and streams worldwide are impacted by pharmaceuticals originating from sewage. The hyporheic zone underlying streams is often regarded as reactive bioreactor with the potential for eliminating such sewage-born micropollutants. The present study aims at checking the elimination potential and analyzing the coupling of hydrodynamics, biogeochemistry and micropollutant processing. To this end, two sites at the lowland stream Erpe, which receives a high sewage burden, were equipped and sampled with nested piezometers. From temperature depth profiles we determined that at one of the sites infiltration of surface water into the aquifer occurs while exfiltration dominates at the other site. Biogeochemical data reveal intense mineralization processes and strictly anoxic conditions in the streambed sediments at both sites. Concentrations of the pharmaceuticals indomethacin, diclofenac, ibuprofen, bezafibrate, ketoprofen, naproxen and clofibric acid were high in the surface water and also in the subsurface at the infiltrating site. The evaluation of the depth profiles indicates some attenuation but due to varying surface water composition the evaluation of subsurface processes is quite complex. Borate and non-geogenic gadolinium were measured as conservative wastewater indicators. To eliminate the influence of fluctuating sewage proportions in the surface water, micropollutant concentrations are related to these indicators. The indicators can cope with different dilutions of the sewage but not with temporally varying sewage composition.

Molecular acidity: A quantitative conceptual density functional theory description

Accurate predictions of molecular acidity using ab initio and density functional approaches are still a daunting task. Using electronic and reactivity properties, one can quantitatively estimate pKa values of acids. In a recent paper [S. B. Liu and L. G. Pedersen, J. Phys. Chem. A 113, 3648 (2009)], we employed the molecular electrostatic potential (MEP) on the nucleus and the sum of valence natural atomic orbital (NAO) energies for the purpose. In this work, we reformulate these relationships on the basis of conceptual density functional theory and compare the results with those from the thermodynamic cycle method. We show that MEP and NAO properties of the dissociating proton of an acid should satisfy the same relationships with experimental pKa data. We employ 27 main groups and first to third row transition metal-water complexes as illustrative examples to numerically verify the validity of these strong linear correlations. Results also show that the accuracy of our approach and that of the conventional method through the thermodynamic cycle are statistically similar.