Prediction of the Dissociation Constant pKa of Organic Acids from Local Molecular Parameters of Their Electronic Ground State

Predicting pKa Values of Quinols and Related Aromatic Compounds with Multiple OH Groups

Quinonoid compounds play central roles as redox-active agents in photosynthesis and respiration and are also promising replacements for inorganic materials currently used in batteries. To design new quinonoid compounds and predict their state of protonation and redox behavior under various conditions, their pK_a values must be known. Methods that can predict the pK_a values of simple phenols cannot reliably handle complex analogues in which multiple OH groups are present and may form intramolecular hydrogen bonds. We have therefore developed a straightforward method based on a linear relationship between experimental pK_a values and calculated differences in energy between quinols and their deprotonated forms. Simple adjustments allow reliable predictions of pK_a values when intramolecular hydrogen bonds are present. Our approach has been validated by showing that predicted and experimental values for over 100 quinols and related compounds differ by an average of only 0.3 units. This accuracy makes it possible to select proper pK_a values when experimental data vary, predict the acidity of quinols and related compounds before they are made, and determine the sites and orders of deprotonation in complex structures with multiple OH groups.

QSPR models for predicting the adsorption capacity for microplastics of polyethylene, polypropylene and polystyrene

Microplastics have become an emerging concerned global environmental pollution problem. Their strong adsorption towards the coexisting organic pollutants can cause additional environmental risks. Therefore, the adsorption capacity and mechanisms are necessary information for the comprehensive environmental assessments of both microplastics and organic pollutants. To overcome the lack of adsorption information, five quantitative structure–property relationship (QSPR) models were developed for predicting the microplastic/water partition coefficients (log K_d) of organics between polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. All the QSPR models show good fitting ability (R² = 0.811–0.939), predictive ability (Q²_ext = 0.835–0.910, RMSE_ext = 0.369–0.752), and robustness (Q_cv² = 0.882–0.957). They can be used to predict the K_d values of organic pollutants (such as polychlorinated biphenyls, chlorobenzene, polycyclic aromatic hydrocarbons, antibiotics perfluorinated compounds, etc.) under different pH conditions. The hydrophobic interaction has been indicated as an important mechanism for the adsorption of organic pollutants to microplastics. In sea waters, the role of hydrogen bond interaction in adsorption is considerable. For polystyrene, π–π interaction contributes to the partitioning. The developed models can be used to quickly estimate the adsorption capacity of organic pollutants on microplastics in different types of water, providing necessary information for ecological risk studies of microplastics.

Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model

The diffusion coefficient (D) is a key physicochemical parameter for the diffusive gradients in thin films technique (DGT) for environmental sampling, which has been extended to organic chemicals (so called o-DGT). D can be measured in the laboratory, although for organic chemicals this parameter might be predicted based on chemical structure. Here we developed for the first time a Quantitative Structure-Property Relationship (QSPR) model to predict the D values. Twenty quantum chemical descriptors that quantify the electronic and energy properties of 120 organic compounds were selected together with molecular mass, solubility and hydrophobicity. The best QSPR model was established by using genetic algorithm and multiple linear regression (GA-MLR). The results indicated that the model derived from the average molecular polarizability (α), the chemical potential (ξ) and the global electrophilicity index (ω) could explain the diffusion of organics in o-DGT and had good statistical performance (R² = 0.767, RMSE = 0.101). Different validation strategies confirmed that the developed model was robust and predictive. 93% of tested compounds were within the applicability domain (AD) and predicted accurately. We concluded that the proposed QSPR model can serve as an efficient predictive tool for new chemicals in the AD, would be useful to cross validate measured D values and provide a better the understanding of the diffusive behaviour of organics in o-DGT and measurements in the environment. It might also be useful in the non-target analysis with o-DGT for chemicals without measured D values.

In silico study for inhibiting thyroid hormone sulfotransferase activity by halogenated phenolic chemicals

Thyroid hormones (THs) are essential to proper growth and development of human bodies. Inhibiting the sulfation metabolism of THs has been demonstrated to be an important way for some environmental pollutants, such as halogenated phenolic compounds, to interfere THs homeostasis, thereby causing health problems. However, the important property characteristics that govern the sulfation inhibition of these chemicals are not well understood, and the experimental data on inhibition potential is limited. In this work, an in silico approach was developed to investigate the structure-activity relationship for their sulfotransferases (SULTs) inhibition. A series of quantum chemical descriptors that quantify the electronic and energy properties of 22 halogenated phenolic compounds have been calculated to establish a predictive model and analyzed their corresponding contributions to SULTs inhibition. Density functional theory (DFT) B3LYP/6-31G** has been employed to optimize molecular geometries to obtain a total of 15 descriptors for every compound. The implementation of linear regression shows three descriptors that represent molecular mass, positive charges on hydrogen atoms, and energy of frontier orbitals strongly correlate with SULTs inhibition potential. This indicates molecular size, hydrogen-bond strength, and nucleophilic-electrophilic reactivity may play important roles in SULTs inhibition. The derived regression model has good statistical performance (r² = 0.84, rms = 0.35), and different validation strategies indicate it can serve as an efficient predictive tool for other chemicals in application domain but with no experimental data, consequently assisting in their THs sulfation inhibition and health risk assessment.

Modeling and predicting pKa values of mono-hydroxylated polychlorinated biphenyls (HO-PCBs) and polybrominated diphenyl ethers (HO-PBDEs) by local molecular descriptors

Hydroxylated polychlorinated biphenyls (HO-PCBs) and polybrominated diphenyl ethers (HO-PBDEs) are attracting considerable concerns because of their multiple endocrine-disrupting effects and wide existence in environment and organisms. The hydroxyl groups enable these chemicals to be ionizable, and dissociation constant, pKa, becomes an important parameter for investigating their environmental behavior and biological activities. In this study, a new pKa prediction model was developed using local molecular descriptors. The dataset contains 21 experimental pKa values of HO-PCBs and HO-PBDEs. The optimized geometries by ab initio HF/6-31G(∗∗) algorithm were used to calculate the site-specific molecular readiness to accept or donate electron charges. The developed model obtained good statistical performance, which significantly outperformed commercial software ACD and SPARC. Mechanism analysis indicates that pKa values increase with the charge-limited donor energy EQocc on hydroxyl oxygen atom and decrease with the energy-limited acceptor charge QEvac on hydroxyl hydrogen atom. The regression model was also applied to calculate pKa values for all 837 mono-hydroxylated PCBs and PBDEs in each class, aiming to provide basic data for the ecological risk assessment of these chemicals.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.