Energy Descriptors Research Articles

Interactions in flavanone and chalcone derivatives: Hirshfeld surface analysis, energy frameworks and global reactivity descriptors.

The present study examines a series of flavanone and chalcone derivatives substituted with electron-withdrawing groups (Cl or Br) and electron-donating groups (OH, CH3 and OCH3), namely, 7-methoxy-2-phenyl-3,4-dihydro-2H-1-benzopyran-4-one, C16H14O3, 2-(4-methoxyphenyl)-3,4-dihydro-2H-1-benzopyran-4-one, C16H14O3, 2-(4-methoxyphenyl)-6-methyl-3,4-dihydro-2H-1-benzopyran-4-one, C17H16O3, 2-(4-chlorophenyl)-3,4-dihydro-2H-1-benzopyran-4-one, C15H11ClO2, 8-bromo-6-methyl-2-phenyl-3,4-dihydro-2H-1-benzopyran-4-one, C16H13BrO2, (2E)-1-(2-hydroxyphenyl)-3-(4-methoxyphenyl)prop-2-en-1-one, C16H14O3, and (2E)-1-(2-hydroxyphenyl)-3-(4-hydroxyphenyl)prop-2-en-1-one, C15H12O3. It compares the two groups of derivatives with regard to their intermolecular interactions in the crystal lattice and lattice energy calculations, together with energy framework visualization and global reactivity descriptors (chemical hardness, chemical potential and electrophilicity index). It also discusses the relationships between different noncovalent interactions derived from Hirshfeld surface analysis, crystal lattice energy and global reactivity descriptors of the compounds.

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

Computational Investigation of Drug Phototoxicity: Photosafety Assessment, Photo-Toxophore Identification, and Machine Learning.

One of the most appreciated capabilities of computational toxicology is to support the design of pharmaceuticals with reduced toxicological hazard. To this end, we have strengthened our drug photosafety assessments by applying novel computer models for the anticipation of in vitro phototoxicity and human photosensitization. These models are typically used in pharmaceutical discovery projects as part of the compound toxicity assessments and compound optimization methods. To ensure good data quality and aiming at models with global applicability we separately compiled and curated highly chemically diverse data sets from 3T3 NRU phototoxicity reports (450 compounds) and clinical photosensitization alerts (1419 compounds) which are provided as supplements. The latter data gives rise to a comprehensive list of explanatory fragments for visual guidance, termed phototoxophores, by application of a Bayesian statistics approach. To extend beyond the domain of well sampled fragments we applied machine learning techniques based on explanatory descriptors such as pharmacophoric fingerprints or, more important, accurate electronic energy descriptors. Electronic descriptors were extracted from quantum chemical computations at the density functional theory (DFT) level. Accurate UV/vis spectral absorption descriptors and pharmacophoric fingerprints turned out to be necessary for predictive computer models, which were both derived from Deep Neural Networks but also the simpler Random Decision Forests approach. Model accuracies of 83-85% could typically be reached for diverse test data sets and other company in-house data, while model sensitivity (the capability of correctly detecting toxicants) was even better, reaching 86%-90%. Importantly, a computer model-triggered response-map allowed for graphical/chemical interpretability also in the case of previously unknown phototoxophores. The photosafety models described here are currently applied in a prospective manner for the hazard identification, prioritization, and optimization of newly designed molecules.

A DFT and MD study of reactive, H2 adsorption and optoelectronic properties of graphane nanoparticles – An influence of boron doping

Structural, reactive, H2 adsorption and charge transfer properties of graphane nanoparticles modified with boron atom have been investigated using the combination of density functional theory (DFT) calculations and molecular dynamics (MD) simulations. Studies of reactivity encompassed calculations of molecular electrostatic potential (MEP) and average local ionization energy (ALIE) descriptors. It has been demonstrated by these quantum-molecular descriptors that reactivity of studied systems significantly increases in the near vicinity of boron dopants. Adsorption properties of studied graphane and its derivative nanoparticles have been performed towards the hydrogen molecules, in order to assess the potential of studied structures in the area of clean energy sources. Results indicate that introduction of boron atoms greatly improves the H2 binding energies. Symmetry-adapted perturbation theory (SAPT) calculations enabled us to decompose the interaction energy to physical contributions and to better understand the influence of boron atoms to the H2 adsorption properties. Charge transfer rates and charge mobilities have been calculated according to Marcus semi-empiric approach. Optoelectronic properties of studied nanostructures have been compared to that of pentacene molecule, in order to further draw attention to their potential for practical applications in materials science area. The influence of size of the graphane nanoparticles was also taken into account and calculations show that both reorganization energies and ΔE(S1−T1) parameter decrease with the increase in size. In case of the largest pristine graphane nanoparticle considered in this work, the electron reorganization energy is lower than in case of the pentacene, while ΔE(S1−T1) parameter has very low value of just 0.05 eV.

To Pass or Not To Pass: Predicting the Blood-Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory.

Predicting the ability of chemical species to cross the blood–brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory. We have been able to show, for the first time, that the computed excess chemical potential in different solvents can be successfully used to predict permeability of compounds in a binary manner (yes/no) via a minimum-descriptor-based model. Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure–activity relationship modeling. The workflow presented in this work is simple enough to be used by nonexperts with ease.

Parameter optimization of a multiscale descriptor for shape analysis on healthcare image datasets

Shape analysis is a key task in computer vision, and multiscale descriptors can significantly enhance shape characterization. However, these descriptors often rely on parameter adjustments to configure a meaningful set of scales that can enable shape analysis. Parameter adjustment in large image datasets is often done on a trial-and-error basis, and an alternative solution to mitigate such a limitation is the use of metaheuristic optimization. The main contribution of this paper is to provide a strategy that supports the automatic parameter adjustment of a multiscale descriptor within a metaheuristic optimization algorithm, where the choice of the cost function strongly influences and boosts the performance of the shape description, which is closely related to the problem domain, i.e. the image dataset. Our research considers synthetic data in a prior evaluation of the cost functions that optimize the scale parameters of the Normalized Multiscale Bending Energy (NMBE) descriptor through the Simulated Annealing (SA) metaheuristic. The cost functions that drive this metaheuristic are: Silhouette (SI), the Davies–Bouldin index (DB) and the Calinski-Harabasz index (CH). We conduct content-based image retrieval and classification experiments to assess the optimized descriptor using three healthcare image datasets: Amphetamine Type Stimulants (ATS) pills (Illicit Pills), pills from the National Library of Medicine (NLM Pills) and hand alphabet gestures (Hands). We also provide segmentation masks for Illicit Pills to guarantee reproducibility. We report the results of tests using a state-of-art method based on a deep neural network, Inception-ResNet-v2. The optimized NMBE with SI and DB achieved competitive and accurate values of above 94%, in terms of both the Mean Average Precision measure (MAP) and Accuracy (ACC) for Illicit Pills and NLM Pills. The precision recall curves demonstrate that it outperforms the Inception-ResNet-v2 for both of these datasets.

Embodied Energy and CO2 Emissions of Widely Used Building Materials: The Ethiopian Context

Buildings use a wide range of construction materials, and the manufacturing of each material consumes energy and emits CO2. Several studies have already been conducted to evaluate the embodied energy and the related CO2 emissions of building materials, which are mainly based on case studies from developed countries. There is a considerable gap in cases of developing countries regarding assessment of embodied energy and CO2 emissions of these building materials. This study identified the top five most used construction materials (cement, sand, coarse aggregates, hollow concrete blocks, and reinforcement bars), which are also prime sources of waste generation during construction in the Ethiopian building construction sector. Then, what followed was the evaluation of the embodied energies and CO2 emissions of these materials by examining five commercial and public buildings within the cradle-to-site lifecycle boundary. The evaluation results demonstrated that cement, hollow concrete blocks (HCB), and reinforcement bars (rebars) are the major consumers of energy and major CO2 emitters. Cumulatively, they were responsible for 94% of the embodied energy and 98% of the CO2 emissions. The waste part of the construction materials has inflated the embodied energy and the subsequent CO2 emissions considerably. The study also recommended several strategies for the reduction of embodied energy and the related CO2 emissions. The research delivers critical insights into embodied energy and CO2 emissions of the five most used building materials in the Ethiopian construction industry, as there are no prior studies on this theme. This might be a cause to arouse awareness and interest among the policy makers and the wider public to clearly understand the importance of research on this crucial issue to develop national energy and CO2 descriptors for construction materials, in order to take care of our naturally endowed, but yet fragile, human habitat.

Balancing Competing Reactions in Hydride Transfer Catalysis via Catalyst Surface Doping: The Ionization Energy Descriptor.

Hydride transfer (HT) is ubiquitous in catalytic reduction reactions. In heterogeneous electrocatalysis, the hydride donor could be a molecular catalytic intermediate adsorbed on an electrode surface. The stability and hydride-donating capability of such an intermediate may determine overall catalytic efficiency. Here, we report how to fine-tune a hydride donor's performance via doping an electrode surface. For semiconductor electrodes, we find that the ionization energy of the surface dopant can serve as a good descriptor for both the stability and hydride-donating capability of the catalytic intermediate adsorbed on the doped site. For the specific case of CO2 reduction on p-GaP, where adsorbed 2-pyridinide (2-PyH-*) was predicted to be the most likely hydride-donating species, we predict that its catalytic performance should be particularly enhanced by substituting Ga with Ti on the electrode surface; Sc, Al, and V surface dopants also could be worthy of further investigation.

Development of software for prediction and virtual screening of antioxidant activity of new synthesized azaheterocyclic compounds

Background: Increasing introduction of innovation technologies into medicine and pharmacology makes it possible to modify and develop further the methods of the development of new medicines. Such methods include virtual screening. In this article approaches to building software for virtual screening of nitric oxide (NO)scavengers in an important class of azaheterocyclic compounds are discussed.Methods: During the study period (from October 2017 to January 2019) the methods for the evaluation of antioxidant activity, as well as quantum-mechanical and statistical calculations were conducted. Quantum-mechanical calculations on HOMO (highest occupied molecular orbital) energy and LUMO (lowest unoccupied molecular orbital) energy descriptors have been conducted using program complex WinMopac 7.2.Results: Evaluation methods of antioxidant activity, quantum-mechanical and statistical calculations have been presented. The algorithm for computer program of virtual screening has been proposed. Prospects and benefits of computer modeling of agent activity are considered.Conclusions: Establishment of computer program for virtual screening will significantly reduce time and resources during researching new synthesized compounds. The algorithm for a computer program of virtual screening has been developed Predictable antioxidant activity data on nitric oxide radical (NO·)inhibition may be calculated in percentage using HOMO and LUMO as the input data for this program.

Predicting skin permeability using the 3D-RISM-KH theory based solvation energy descriptors for a diverse class of compounds.

The state-of-the-art molecular solvation theory is used to predict skin permeability of a large set of compounds with available experimental skin permeability coefficient (logKP). Encouraging results are obtained pointing to applicability of a novel quantitative structure activity model that uses statistical physics based 3D-RISM-KH theory for solvation free energy calculations as a primary descriptor for the prediction of logKP with relative mean square error of 0.77 units.

In silico study combining QSAR, docking and molecular dynamics simulation on 2,4-disubstituted pyridopyrimidine derivatives

2,4-Disubstituted pyridopyrimidine derivatives were studied against ABCG2 enzyme. The modeling of pyridopyrimidine derivatives were done using two methods of multiple linear regression and support vector regression and four molecular descriptors of BIC4, log p, VRA2, and binding energy were selected for modeling. The statistical results were satisfactory. The interactions of ABCG2 enzyme with pyridopyrimidine derivatives were investigated using molecular docking method. Based on the results, increasing of binding energy and hydrophobicity of the compounds increase their inhibitory activity. Protein stability in complex with pharmaceutical derivatives was discussed using molecular dynamics simulation method.

Decision Tree for Mechanism of Antitumor Drugs Action Prediction

Classification by the mechanism of action of antitumor drugs using quantitative descriptors has been carried out in this study. The dataset of drugs includes 115 compounds with known activity and mechanism of action. The structures have been taken from the National Cancer Institute database. The dataset includes: 30 structures with the mechanism of alkylation action, 23 compounds are topoisomerase I inhibitors, 16 structures are topoisomerase II inhibitors, 17 compounds are DNA/RNA antimetabolites (dihydrofolate reductase inhibitors), 16 molecules are DNA antimetabolites, and 13 compounds are antimitotic drugs. A decision tree has been constructed for determination of each class of the compounds using 3D descriptors that have been computed by MERA software. Analysis of the results has shown that each mechanism of drug action is characterized by a set of descriptors. Important quantum-chemical descriptors of structures with mechanism for alkylation action, DNA/RNA antimetabolites, and DNA antimetabolites have been determined. Quantum-chemical and geometric descriptors for structures of topoisomerase II inhibitors and geometric descriptors for structures of topoisomerase I inhibitors have been established. An important energy descriptor for antimitotic drugs has been determined. The typical descriptor values for active and inactive structures for each mechanism of action have been determined. The quality recognition of active structures for each mechanism of drugs action has been determined. The highest recognition quality value of active and inactive compounds is observed in the decision tree of topoisomerase II inhibitors. The minimal recognition quality value of active and inactive compounds is observed in the decision tree of DNA/RNA-antimetabolites. The suggested decision trees can be used for determination of the action mechanism of antitumor drugs.

Computational Study of Methane Activation on γ-Al2O3.

The C–H activation of methane remains a longstanding challenge in the chemical industry. Metal oxides are attractive catalysts for the C–H activation of methane due to their surface Lewis acid–base properties. In this work, we applied density functional theory calculations to investigate the C–H activation mechanism of methane on various sites of low-index facets of γ-Al2O3. The feasibility of C–H activation on different metal–oxygen (acid–base) site pairs was assessed through two potential mechanisms, namely, the radical and polar. The effect of surface hydroxylation on C–H activation was also investigated to examine the activity of γ-Al2O3 under realistic catalytic surface conditions (hydration). On the basis of our calculations, it was demonstrated that the C–H activation barriers for polar pathways are significantly lower than those of the radical pathways on γ-Al2O3. We showed that the electronic structure (s- and p-band center) for unoccupied and occupied bands can be used to probe site-dependent Lewis acidity and basicity and the associated catalytic behavior. We identified the dissociated H2 binding and final state energy as C–H activation energy descriptors for the preferred polar pathway. Finally, we developed structure–activity relationships for the C–H activation of methane on γ-Al2O3 that account for surface Lewis acid–base properties and can be utilized to accelerate the discovery of catalysts for methane (and shale gas) upgrade.

Effect of Substitutionally Doped Graphene on the Activity of Metal Nanoparticle Catalysts for the Hydrogen Oxidation Reaction

Increasing the activity of non-noble catalysts for hydrogen oxidation is crucial in enhancing the efficiency of hydroxide exchange membrane fuel cells. Herein, we study the impact of graphene and nitrogen- and boron-doped graphene supports on the hydrogen oxidation reaction occurring on Ni, Cu, and Ag nanoparticles using first-principles calculations and published experimental data. We find that doping of graphene leads to a stronger interaction between the nanoparticle and the support, consequently weakening hydrogen adsorption. This leads to increased activity of supported Ni nanoparticles, but decreased activity of supported Cu and Ag nanoparticles. The dopant-induced changes in the hydrogen adsorption energies are quantitatively as important as the adsorption site. To describe adsorption energies for each supported nanoparticle, principal component analysis is introduced to systematically identify molecular descriptors of adsorption energy. Finally, a size-dependent activity model is formulated to clo...

Lewis Acidity and Basicity of Mixed Chlorometallate Ionic Liquids: Investigations from Surface Analysis and Fukui Function.

Mixed chlorometallate ionic liquids (ILs) have been regarded as potential solvents, catalysts, and reagents for many organic processes. The acidity and basicity of these ILs were correlated with theoretically estimated parameters such as electrostatic surface potential maxima and minima, average local surface ionization energy, and Fukui and dual descriptor functions. The introduction of metal chloride into the anions would influence the acidity/basicity of ILs by withdrawing the electron density from the cationic counterpart. For the [C4mim]-based ILs with the mixed-metal anions, the acidity tends to attenuate while the basicity becomes stronger, as compared to the corresponding chloroaluminate ILs. However, the acidity of [(C2H5)3NH]-based ILs with the mixed-metal anions are greater than that of the net chloroaluminate ILs. The Fukui function values showed that most of the mixed chlorometallate ILs belong to bifunctional distribution. The mixed chlorometallate ILs both have electrophilic and nucleophilic sites, which would be beneficial for their applications.

Structure–Mechanics and Compressibility Profile Study of Flufenamic Acid:Nicotinamide Cocrystal

A contribution of crystal structure, mechanical moduli, and macroscopic compression properties of flufenamic acid (FFA) and its cocrystal with nicotinamide (NIC) was evaluated to predict their compaction performance. The FFA:NIC cocrystal formation was confirmed using differential scanning calorimetry, powder X-ray diffraction, and Fourier transform infrared. FFA:NIC compaction performance was compared with its coformers. Attachment energies (Eatt) with lowest absolute energy slip planes were calculated from reported crystal structures. Powder Brillouin light scattering was used to measure the mechanical moduli, while macroscopic compression performance was evaluated with “in-die” Heckel and compression energy descriptors. The absolute Eatt were found in the following ascending order: NIC < FFA:NIC < FFA. These materials can be arranged with their increased stiffness as FFA < FFA:NIC < NIC based on their elastic moduli. A relatively soft and elastic FFA showed highest compressibility but poor tabletabilit...

GRP78-targeted in-silico virtual screening of novel anticancer agents.

Overexpression of GRP78 in a variety of cancers such as glioblastoma, leukemia, lung, prostate, breast, gastric, and colon makes it a prime target for anticancer drug development. Present study reports GRP78-based design of novel anticancer agents using in-silico methods. As a first step toward the work, the interactions between GRP78 and 15 known ligands were modeled by docking simulation. The docked complex, GRP78-13, superior to other compounds with respect to its experimental activity and energy descriptors, was deduced into a structure-based pharmacophore. This hypothesis was applied as a screening filter to Asinex and Chemdiv databases. Finally, 23 hits were tested in vitro. Among these, VH1019 and VH1011 induced a concentration-dependent strong broad antiproliferative effect in glioma (U87-MG), breast cancer (MCF-7), and prostate cancer (DU-145) cell lines as compared to nontumorigenic control, neonatal foreskin fibroblast (HFF-1). These compounds showed preferential growth inhibition of cancer cells over normal cells. The acetohydrazide derivative VH1019 was identified as a potential new chemotype for GRP78 inhibitors with an IC50 of 12.7μM in MCF-7.

Effective Smoke Detection Using Spatial-Temporal Energy and Weber Local Descriptors in Three Orthogonal Planes (WLD-TOP)

Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was implemented in MATLAB 8.1.0.604 (R2013a). Accuracy of 99.30%, detection rate of 99.28% and false alarm rate of 0.65% were obtained when tested with some online videos. The output of this work would find applications in early fire detection systems and other applications such as robot vision and automated inspection.

Estimating the energy of intramolecular bifurcated (three-centered) hydrogen bond by X-ray, IR and 1H NMR spectroscopy, and QTAIM calculations

The structure of a series of the 2-substituted pyrroles containing the intramolecular NH⋯OC hydrogen bond and the 2,5-disubstituted pyrroles having the three-centered intramolecular CO⋯H(N)⋯OC hydrogen bond was studied by X-ray, IR, NMR spectroscopy, and quantum chemical calculations, including calculations in terms of Quantum Theory of Atoms in Molecules (QTAIM). The X-ray diffraction data show that the intramolecular rH … O and RN … O distances increase upon conversion from a two-centered hydrogen bond to a three-centered hydrogen bond, indicating a weakening of the last interaction. The topological parameters of the hydrogen bond obtained from the QTAIM calculations (electron density ρBCP and potential energy density V at the critical point of the hydrogen bond) made it possible to quantify the hydrogen bond energy in the studied compounds. The energy of intramolecular NH⋯OC hydrogen bond in the 2-substituted pyrroles is in the range from 5.5 to 6.0 kcal mol−1 (moderate hydrogen bond), whereas the energy of the components of the three-centered intramolecular CO⋯H(N)⋯OC hydrogen bond in the 2,5-disubstituted pyrroles is reduced to 2.5–3.5 kcal mol−1 (weak hydrogen bond). For weak components of the three-centered hydrogen bond, the principle of the additivity of effects on the spectral parameters influenced by the hydrogen bond (change in the vibration frequency of the NH bond, ΔνN‒H, and the chemical shift of bridge proton, ΔδNH) was proposed. For this reason, the ½ΔνN-H and ½ΔδNH values also were used for estimating the energy of the two-centered components of the three-centered CO⋯H(N)⋯OC hydrogen bond. These estimates are in good agreement with those obtained from the ρBCP and V parameters. Consequently, the ½ΔνN-H and ½ΔδNH parameters can be used as an energy descriptor for two-centered components in the case of a symmetric intramolecular three-centered hydrogen bond.

Study of dependence of xanthine derivatives NO-scavenger properties from energy descriptors

Study of dependence of xanthine derivatives NO-scavenger properties from energy descriptors