The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds

Fei, Jiangchi; Mao, Qiming; Lu, Peng; Ye, Tiantian; Yang, Yuan; Luo, Shuang

doi:10.3390/molecules23112935

Cited by 13 publications

(7 citation statements)

References 51 publications

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“…Choosing appropriate molecular descriptors, such as the octanol−water partition coefficient (K OW ) and dissociation constant (pK a ), is a popular technique for compiling the structure information and physicochemical attributes of organic compound into digital form in QSAR modeling. 25,26 However, we found that ln k obs exhibited poor correlations to ln K OW in manganese oxo-anion oxidation (R 2 < 0.22), suggesting that the hydrophobic nature of PC was not responsible for the selective oxidation of it by manganese oxoanions (Figure S4). In addition, the pK a descriptor showed weak associations for the kinetic rates of PC oxidation by manganese oxo-anions (R 2 < 0.70, Figure S5), implying that proton transfer was not the prevailing mechanism for these oxidation processes.…”

Section: Chemicalsmentioning

confidence: 86%

Unraveling the Role of Humic Acid in the Oxidation of Phenolic Contaminants by Soluble Manganese Oxo-Anions

Wang,

Chai,

et al. 2024

Environ. Sci. Technol.

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Humic acid (HA) is ubiquitous in natural aquatic environments and effectively accelerates decontamination by permanganate (Mn(VII)). However, the detailed mechanism remains uncertain. Herein, the intrinsic mechanisms of HA's impact on phenolics oxidation by Mn(VII) and its intermediate manganese oxo-anions were systematically studied. Results suggested that HA facilitated the transfer of a single electron from Mn(VII), resulting in the sequential formation of Mn(VI) and Mn(V). The formed Mn(V) was further reduced to Mn(III) through a double electron transfer process by HA. Mn(III) was responsible for the HA-boosted oxidation as the active species attacking pollutants, while Mn(VI) and Mn(V) tended to act as intermediate species due to their own instability. In addition, HA could serve as a stabilizer to form a complex with produced Mn(III) and retard the disproportionation of Mn(III). Notably, manganese oxo-anions did not mineralize HA but essentially changed its composition. According to the results of Fourier-transform ion cyclotron resonance mass spectrometry and the second derivative analysis of Fourier-transform infrared spectroscopy, we found that manganese oxo-anions triggered the decomposition of C−H bonds on HA and subsequently produced oxygen-containing functional groups (i.e., C−O). This study might shed new light on the HA/manganese oxo-anion process.

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Section: Chemicalsmentioning

confidence: 86%

Unraveling the Role of Humic Acid in the Oxidation of Phenolic Contaminants by Soluble Manganese Oxo-Anions

Wang,

Chai,

et al. 2024

Environ. Sci. Technol.

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“…For example, the specific energy features mainly focus on the energy change in the protein–ligand recognition process. Similarly, the quantum chemical descriptors 139 of molecules are inextricably linked to the inner energy, such as the energy of the highest occupied molecular orbital ( E HOMO ), the energy of the lowest unoccupied molecular orbital ( E LUMO ), the energy difference between those orbitals ( E GAP ), electronegativity ( χ ), and so on. The protein–ligand interaction fingerprint is also proposed in imitation of molecular fingerprints.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Featurization strategies for protein–ligand interactions and their applications in scoring function development

Xiong

Shen

Yang

et al. 2021

WIREs Comput Mol Sci

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The predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein–ligand interactions and to obtain machine‐readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated‐extraction features. Moreover, the automated‐extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein–ligand interactions in the context of MLSFs, with emphasis on the recently rising automated‐extraction features. We also discuss the similarity between protein–ligand interaction representations and small‐molecule representations, and the challenges confronted by the scientific community in characterizing protein–ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Interactions

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“…Intermolecular electrostatic descriptors bonding of hydrogen solvent-accessible negatively or positively charged atomic surface regions have also been used as a data source. As derivative quantities like absolute hardness, the energies of the highest occupied and lowest empty molecule orbitals form important quantum chemical descriptors [33][34][35][36][37][38] .…”

Section: Electrostaticmentioning

confidence: 99%

Untitled

2023

IJPSR

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Structure-Activity relationship (SAR) only identifies the chemical group responsible for producing the target biological effect in the organism. It was first present in 1865, structural activity relationship was refined dates back to the nineteenth century and now it has advanced from QSAR to 3D QSAR. Quantitative Structure-Activity Relationships (QSAR) are attempted to correlate the structural and biological properties of compounds or molecules. QSAR is used in drug design and medicinal chemistry. By using QSAR, scientists predefined toxicity related to organic molecules and determined the perfect potential biological activity that helps produce drug moiety. Group Based-Quantitative Structure-Activity Relationship (G-QSAR) is fragment dependent method which bases on molecule descriptors. Statistical parameters validate the GQSAR method. The cross-term fragment descriptor is used in GQSAR to study the relation of molecular fragments and variation in biological-based response. It provides a clue for designing new molecules and predicting their activity. In this article, we mainly concentrate on the various QSAR model such as Hansch Analysis, Free Wilson Analysis, various physicochemical properties, QSAR development process, QSAR model, and methodology of the GQSAR model, implementation and working as well as some general aspects in QSAR and GQSAR study.

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The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds

Cited by 13 publications

References 51 publications

Unraveling the Role of Humic Acid in the Oxidation of Phenolic Contaminants by Soluble Manganese Oxo-Anions

Unraveling the Role of Humic Acid in the Oxidation of Phenolic Contaminants by Soluble Manganese Oxo-Anions

Featurization strategies for protein–ligand interactions and their applications in scoring function development

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