Quantum Mechanical Descriptors Research Articles

Potential applications of armchair, zigzag, and chiral boron nitride nanotubes as a drug delivery system: Letrozole anticancer drug encapsulation

A density functional theory calculation has been applied to be investigated the ability of (12,12) armchair, (21,0) zigzag, and (18,5) chiral boron nitride nanotubes (BNNT) as the drug nanocarrier in Letrozole delivery. For this purpose, the behavior of drug Letrozole when encapsulated into different chirality of BNNTs with the same diameter was studied. Moreover, the reduced side effects and increasing solubility of Letrozole can be achieved by encapsulation in boron nitride nanotubes. A complete understanding of the encapsulation process of drug molecules into BNNTs is necessary for the development of drug delivery systems. Geometry optimizations of the three types of BNNTs and drug Letrozole have been performed by means of density functional theory calculation, using the functional wb97xd with 6-31G basis set. The results indicate that Letrozole is encapsulated in cavity of the nanotubes. The structural and the electronic properties of encapsulated Letrozole in the studied systems have been also predicted by quantum mechanical descriptors such as equilibrium distances, highest occupied molecular orbital (HOMO)—lowest unoccupied molecular orbital (LUMO) energy gap. The quantum mechanical calculations verify that the armchair and the chiral single-walled boron nitride nanotubes could spontaneously adsorb Letrozole in a chemisorption process while the physisorption process plays a large role in adsorption within the zigzag single-walled boron nitride nanotube.

Computational study of the binding mode, action mechanism and potency of pregabalin through molecular docking and quantum mechanical descriptors

In the present study, we performed a computational study to gain insights on the binding mode and high affinity of pregabalin, its inactive isomer (R-pregabalin) and gabapentin when modulating voltage-gated calcium channels. Quantum chemical descriptors were evaluated at two different levels of theory (ωB97XD and B3LYP-D3) for the three molecules. The results show that the three ligands have similar quantum chemical descriptors, suggesting that the affinity is governed by the binding pose and the ability to access the pocket. The binding mode analysis of pregabalin indicates that it is interacting with 12 residues (6 hydrogen bonds) including Arg217, which is key to pregabalin action mechanism. Our results suggest that the electrostatic interactions and the hydrogen bonds between pregabalin and Arg217 could explain its high affinity, highlighting the importance of Arg217 in the pharmacological action.

Concentration-dependent adsorption of organic contaminants by graphene nanosheets: quantum-mechanical models.

Adsorption is the key process in the expression of environmentally relevant physicochemical and toxicological properties of carbon nanomaterials. However, the adsorption of organic contaminants on to nanomaterials is a highly complex phenomenon, owing to the heterogeneity of adsorption sites, for example, on graphene surface as well as due to multiple factors operative during the adsorption, particularly, at the quantum-mechanical level. For predicting the concentration-dependent adsorption coefficients of organic contaminants by carbon nanomaterials, one option has been to rely on the existing linear-solvation energy relationship (LSER) models. The present work on the adsorption of aromatic and aliphatic organic contaminants by graphene nanosheets reveals that the existing LSER models are prone to failure when tested for internal and external validation using an external prediction set of compounds unknown to the model. As an alternative to the LSERs, the present work reports pure quantum-mechanical models developed using computational only quantum-mechanical descriptors. The reliability of the quantum-mechanical models was tested using state-of-the-art validation procedures employing an external prediction set of compounds. The proposed quantum-mechanical models reveal mean polarizability, zero-point vibrational energy, and its electron-correlation contribution to be the key descriptors in the prediction of adsorption coefficients of organic contaminants by graphene nanosheets.

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C–H functionalization, aromatic C–X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings.

All-Nitrogen Cages and Molecular Crystals: Topological Rules, Stability, and Pyrolysis Paths

We combined ab initio molecular dynamics with the intrinsic reaction coordinate in order to investigate the mechanisms of stability and pyrolysis of N4 ÷ N120 fullerene-like nitrogen cages. The stability of the cages was evaluated in terms of the activation barriers and the activation Gibbs energies of their thermal-induced breaking. We found that binding energies, bond lengths, and quantum-mechanical descriptors failed to predict the stability of the cages. However, we derived a simple topological rule that adjacent hexagons on the cage surface resulted in its instability. For this reason, the number of stable nitrogen cages is significantly restricted in comparison with their carbon counterparts. As a rule, smaller clusters are more stable, whereas the earlier proposed large cages collapse at room temperature. The most stable all-nitrogen cages are the N4 and N6 clusters, which can form the van der Waals crystals with densities of 1.23 and 1.36 g/cm3, respectively. The examination of their band structures and densities of electronic states shows that they are both insulators. Their power and sensitivity are not inferior to the modern advanced high-energy nanosystems.

Toward Accurate Predictions of Atomic Properties via Quantum Mechanics Descriptors Augmented Graph Convolutional Neural Network: Application of This Novel Approach in NMR Chemical Shifts Predictions.

In this study, an augmented Graph Convolutional Network (GCN) with quantum mechanics (QM) descriptors was reported for its accurate predictions of NMR chemical shifts with respect to experimental values. The prediction errors of 13C/1H NMR chemical shifts can be as small as 2.14/0.11 ppm. There are two crucial characteristics for this modified GCN: in one aspect, such a novel neural network could efficiently extract the overall molecule structure information; in another aspect, it could accurately solve the chemical environment of the target atom. As there exists an imperfect linear regression between the experimental NMR chemical shifts (δ) and the density functional theory (DFT) calculated isotropic shielding constants (σ), the inclusion of QM descriptors within GCN can largely improve its performance. Moreover, few-shot learning also becomes feasible with these descriptors. The success of this novel GCN in chemical shifts predictions also indicates its potential applicability for other computational studies.

Discovering Electron-Transfer-Driven Changes in Chemical Bonding in Lead Chalcogenides (PbX, where X = Te, Se, S, O).

Understanding the nature of chemical bonding in solids is crucial to comprehend the physical and chemical properties of a given compound. To explore changes in chemical bonding in lead chalcogenides (PbX, where X= Te, Se, S, O), a combination of property-, bond-breaking-, and quantum-mechanical bonding descriptors are applied. The outcome of the explorations reveals an electron-transfer-driven transition from metavalent bonding in PbX (X = Te, Se, S) to iono-covalent bonding in β-PbO. Metavalent bonding is characterized by adjacent atoms being held together by sharing about a single electron (ES ≈ 1) and small electron transfer (ET). The transition from metavalent to iono-covalent bonding manifests itself in clear changes in these quantum-mechanical descriptors (ES and ET), as well as in property-based descriptors (i.e., Born effective charge (Z*), dielectric function ε(ω), effective coordination number (ECoN), and mode-specific Grüneisen parameter (γTO )), and in bond-breaking descriptors. Metavalent bonding collapses if significant charge localization occurs at the ion cores (ET) and/or in the interatomic region (ES). Predominantly changing the degree of electron transfer opens possibilities to tailor material properties such as the chemical bond (Z*) and electronic (ε∞ ) polarizability, optical bandgap, and optical interband transitions characterized by ε2 (ω). Hence, the insights gained from this study highlight the technological relevance of the concept of metavalent bonding and its potential for materials design.

Dioxygen Binding to all 3d, 4d, and 5d Transition Metals from Coupled-Cluster Theory.

Understanding how transition metals bind and activate dioxygen (O2 ) is limited by experimental and theoretical uncertainties, making accurate quantum mechanical descriptors of interest. Here we report coupled-cluster CCSD(T) energies with large basis sets and vibrational and relativistic corrections for 160 3d, 4d, and 5d metal-O2 systems. We define four reaction energies (120 in total for the 30 metals) that quantify O-O activation and reveal linear relationships between metal-oxygen and O-O binding energies. The CCSD(T) data can be combined with thermochemical cycles to estimate chemisorption and physisorption energies for each metal from metal oxide embedding energies, in good correlation with atomization enthalpies (R2 =0.75). Spin-geometry variations can break the linearities, of interest to circumventing the Sabatier principle. Pt, Pd, Co, and Fe form a distinct group with the weakest O2 binding. R2 up to 0.84 between surface adsorption energies and our energies for MO2 systems indicate relevance also to real catalytic systems.

Soft Heteroleptic N-Heterocyclic Carbene Palladium(II) Species for Efficient Catalytic Routes to Alkynones via Carbonylative Sonogashira Coupling.

N,N′-Substituted di-isopropyl (NHC-1), benzyl-isopropyl (NHC-2), and adamantyl-isopropyl (NHC-3) benzimidazolium salts react with palladium(II) bromide in pyridine to afford the corresponding trans-dibromidopyridinepalladium(II) complexes Pd-C1, Pd-C2, and Pd-C3 in high yields. A distorted square planar geometry for Pd-C2 and Pd-C3 was confirmed by single-crystal X-ray diffraction. The palladium(II) complexes show a remarkably higher catalytic activity and selectivity, compared to the literature data, in carbonylative Sonogashira coupling reactions of aryl iodides and aryl diiodides with aryl alkynes, alkyl alkynes, and dialkynes. Excellent yields with as low as 0.03 mol % loading of the catalyst were obtained. In the series of benzimidazolium (NHC) precursors, the 1H NMR signals of the α hydrogen show a consistent probing of the N-substituent donor strength. The density functional theory (DFT) quantum mechanical descriptors of the frontier orbitals were calculated. A linear correlation of the calculated absolute softness of the complexes versus the calculated percent buried volume (%Vbur) of their corresponding ligands was obtained. The catalytic activity experimental data are consistent with the hard soft acid base (HSAB)-predicted high affinity of the softest Pd-C3 complex for soft substrates, such as aryl iodides.

Biological perspective of a triazine derivative with isatin/chalcone/acridone: DFT and docking investigations

The article highlights insights into biological activity using computational methods in products of 1,3,5-triazine-2,4-diamine with 1H-indole-2,3-dione (isatin)/(2E)-13-diphenylprop-2-en-1-one (chalcone)/10H-acridin-9-one (acridone). Biological activity is carried out using the method of electron density, 6-311++G(d,p) for molecular and electronic characteristics. Frontier molecular orbitals provide quantum mechanical descriptors to determine electronic properties. Studies show reduced energy gap with high kinetic stability and the index of electrophilicity is a consequence of delocalized sites of 1,3,5-triazine-2,4-diamine and nucleophilic sites are responsible for biological activity. Enhancement in electrophilicity index of products confirms electron acceptor between the reactants. The structural and molecular docking with different proteins shows that the product molecules are good drugs for the corresponding activity.

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles.

Background:Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure–activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.Objectives:By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.Methods:We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta () release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs.Results:Seventeen out of 30 MeONPs induced excess production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive were more likely to cause lysosomal damage and inflammation.Conclusions: released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508

Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials

ABSTRACTGraphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.

Calculation of Angle of Twist, Eigen values and Atomic Orbital Election Population in 2, 3 & 4 Amido Pyrans by DFT method

Density functional theory (DFT) emerged as an important tool in quantum chemistry calculation. Parr introduced a term electronic chemical potential for every chemical entity or system which was given by = E/NV. Some important molecular parameters viz angle of twist, eigen values and AOEP were calculated in Pyran and its derivatives which are helpful in predicting the coordination capacity of the said compounds.Since the energies of HOMO and LUMO are popular quantum mechanical descriptors therefore by comparing the energies of HOMO and LUMO in these compounds the ability of better coordination can be predicted.

Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals

The quantitative activity relationships (QSARs) of the radical scavenging activities of 96 natural antioxidants based on the graph of atomic orbitals by CORAL software. The QSAR models are developed with the representation of the molecular structure by the Simplified Molecular Input-Line Entry System (SMILES) and the Monte Carlo optimization was used for calculation of the correlation weights of optimal SMILES based descriptors. The data set was randomly distributed three times into the training, invisible training, calibration and validation set was examined. The R2 values of the validation set for splits 1 to 3 were 0.966, 0.921, and 0.886 respectively. These models are established using only SMILES information, without any information on physicochemical parameters, the quantum mechanics descriptors, or 3D descriptors of the antioxidant structures. The obtained results indicated that the proposed QSAR models can be successfully used for predictions of the radical scavenging activities of new natural and synthetic antioxidants.

Density functional theory study towards investigating the adsorption properties of the γ-Fe2O3 nanoparticles as a nanocarrier for delivery of Flutamide anticancer drug

In this work, we perform density functional theory studies to comprehend the structure and energetics of the interaction of γ-Fe2O3 nanoparticles with Flutamide (FLU) anticancer drug. Quantum mechanics calculations by two methods including B3LYP/6-31G** and M06-2X/6-31G** have been used to obtain the details of energetic, geometric, and electronic features of the drug molecule interacting with the surface of the maghemite nanoparticles in water solution. The obtained calculations of M06-2X/6-31G** method approved the observation of the strongest adsorption within the hydrogen bond interactions between two considered molecules are predominate, while the adsorption process of drug on the nanoparticles in B3LYP/6-31G** method is endothermic and hence, the adsorbed structures are unstable. The quantum theory of atoms in molecules analysis illustrates closed shell interactions between the drug molecule and the γ-Fe2O3 nanoparticles. The natural bond orbital analysis demonstrated that the drug molecule has the ability to be adsorbed on the nanoparticle surface with the transfer of charge from the drug molecule to maghemite nanoparticles. Moreover, quantum mechanical descriptors within the drug-nanoparticles systems were investigated and it was implied that binding of FLU molecule with γ-Fe2O3 nanoparticles is thermodynamically favorable. Therefore, γ-Fe2O3 nanoparticles can be introduced as efficient systems for the delivery of the drug molecule.

Predicting the Electrochemical Properties of Lithium-Ion Battery Electrode Materials with the Quantum Neural Network Algorithm

Discovery of new inorganic solid materials can be accelerated with the aid of a reliable computational tool for predicting the associated electrochemical properties. Hence, we propose a quantitative structure–property relationship model by combining the three-dimensional (3D) quantum mechanical descriptors of materials and the artificial neural network algorithm, which is termed the 3D-QANN model. 3D distribution of electrostatic potentials (ESPs) in the super cell of each inorganic solid material serves as the unique numerical descriptor to derive the 3D-QANN model. The optimized prediction model is then validated in terms of estimating the discharge energy density (D) and the capacity fading (Q) of lithium-ion battery (LIB) cathode materials with the layered structure. The 3D-QANN model reveals good performance in predicting both D and Q values with high correlation with the corresponding experimental data. This indicates the suitability of the quantum mechanical ESP distribution as the numerical descri...

Quantum-mechanical LSERs for the concentration-dependent adsorption of aromatic organic compounds by activated carbon: Applications and comparison with carbon nanotubes

ABSTRACTCarbon nanotubes (CNTs) have taken precedence over activated carbon in various applications where adsorption is the primary process. The adsorption of chemical compounds by CNTs and activated carbon is most often predicted through linear free energy/solvation energy relationships (LFERs/LSERs). This work proposes quantum-mechanical LSER models based on a combination of quantum-mechanical descriptors and solvatochromic descriptors of LSERs for predicting the adsorption of aromatic organic compounds by activated carbon at varying adsorbate concentrations. The models are validated using state-of-the-art procedures employing an external prediction set of compounds. This work reveals the hydrogen bond donating and accepting ability of compounds to be the most influencing – but a negative – factor in the adsorption process of activated carbon. The quantum-mechanical LSERs proposed in this work are analysed and found to be equally reliable as the existing LSERs. These were further used to predict the adsorption of nucleobases, steroid hormones, agrochemicals, endocrine disruptors and pharmaceutical drugs. Notably, agrochemicals and endocrine disruptors are predicted to be adsorbed more strongly by activated carbon when compared with their adsorption by CNTs. However, quantum-mechanical LSERs predict the adsorption strength of biomolecules on activated carbon to be similar to that on the CNTs, which can be used to assess the risk associated with using carbon materials.

Интегральные характеристики оптических спектров, как новый класс дескрипторов для сложных молекулярных систем

The authors summarized the results on the study of the relationship of physicochemical properties with spectral integral characteristics of complex molecular systems: integral absorption, reflection coefficients, integral oscillator forces, color characteristics, integral autocorrelation functions, signal convolutions defined in the visible or UV spectral regions. It is shown that these integral characteristics are numerical parameters that adequately convey the physicochemical and electronic properties of molecules and their mixtures, so these values can be used as descriptors. Since electronic spectra are unique characteristics of a substance, these descriptors have a high discriminating ability. The classification of such descriptors is given. These values differ from physicochemical descriptors such as absorption maxima, since their determination requires information on the spectrum of these systems without isolating individual bands and maxima using the Fourier transform. In contrast to conventional physicochemical and quantum mechanical descriptors, the proposed descriptors can be applied to the study of multicomponent systems with unknown structure and composition. This makes it possible to use these values to determine the physicochemical properties and averaged over the composition of the electronic characteristics of a substance, such as petroleum distillates, petroleum, biological fluids, and similar mixtures. The paper provides relevant examples, confirmed by statistical data processing. The obtained patterns should be defined as the patterns "spectrum-property" by analogy with the patterns "structure-property", known in organic chemistry and chemical informatics. The consistency of the developed approach is shown, and a quantum-chemical interpretation of these laws is given. Based on the established spectrum-property dependencies, analytical methods can be developed for determining the amount of complex hydrocarbon systems, such as oil and their products, gas condensates, products of high-temperature pyrolysis of organic substances, etc. The established patterns can be applied to determine the ionization potentials and electron affinity of molecules, as well as in medical diagnostics, oil refining, petrochemistry, oil field development technology, and other fields of science and nanotechnology.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs.

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.