Structure Search Research Articles

Identification of 3H-benzo[b] [1,4] diazepine derivatives as PPARα agonists by in silico studies and biochemical evaluation

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the world, whose pathologic features include dysregulated glucose homeostasis and lipid accumulation. Peroxisome proliferators-activated receptor α (PPARα) is a key regulator of fatty acid metabolism and ketogenesis due to its regulatory pathways involve activating fatty acid uptake, accelerating fatty acid oxidation, inhibiting gluconeogenesis, and suppressing inflammation and fibrosis. Therefore, PPARα is considered as a potential target for the treatment of NAFLD and some agonists have entered clinical trials, which drove us to discover more novel PPARα agonists. In current work, new 3H-benzo[b] [1,4] diazepine PPARα agonists were identified from the ChemDiv database by pharmacophore modeling, molecular docking, derivative structure search, and bioassays, where compound LY-2 and its derivatives (LY-10∼LY-19) were discovered to promote the expression of PPARα downstream gene, carnitine palmitoyl transterase-1 α (cpt1α). Among these active compounds, the EC50 value of LY-2 against increasing cpt1α was 2.169 μΜ. Furthermore, the effect of LY-2 on cpt1α was weakened when PPARα knock down, which confirmed that it is a PPARα agonist again. Finally, the results from molecular dynamics simulations and binding free energy calculations showed that π-π stacking and hydrogen bonding interactions played key roles in the binding of LY-2 and PPARα protein and their complex maintained a stable structure to facilitate LY-2 to have a better binding affinity with PPARα protein. Taken together, compound LY-2 might be a novel lead compound for the development of potent PPARα agonists. Communicated by Ramaswamy H. Sarma

A bibliometric analysis deconstructing research on how cognitive technologies affects man-machine collaboration.

This paper addresses knowledge work challenges relating to emerging cognitive technologies. The field of research addressing artificial intelligence (AI), and related topics, is rapidly increasing. However, despite this emerging interest, the currently body of published research remains complex and unstructured. In particular, it remains to be understood how these technologies is implemented and cause changes in man-machine collaboration. To inform this issue, we conducted a bibliometric analysis of extant literature on AI and man-machine collaboration to take stock of extant published research in order to provide a foundation upon which both future theory and practice can be built. We based our analysis of an exhaustive structured literature search of published academic research in Web of Science (WoS) until 2020. Using the keywords digi* AND transform* OR artificial intelligence, 8 728 articles were identified. The bibliometric analysis enabled us first to identify 202 relevant articles published within the fields of business and management, and subsequently to further narrowing our scope to 25 core contributions using bibliometric coupling. A content analysis of these 25 articles revealed that whereas there is a lot of attention to the technological complexities related to the emerging cognitive technologies, there is to date limited empirical descriptions of the consequences for individuals, organizations or value creation of adopting these technologies. Our study identifies four important dimensions of man-machine collaboration; Knowledge worker, Organization, Market, and Society. Moreover, our findings reveal extant research is inconclusive with respect to the forces affecting these dimensions as different authors record both proactive forces and constraining forces associated with each of the four dimensions. Our contribution, as well as, the identification of a core canon of relevant research articles provides a foundation upon which future research and practice can be built by identifying core dimension and the forces acting upon them.

Structural Evolution and Electronic Properties of V2Sin-/0 (n = 7-14) Clusters: Anion Photoelectron Spectroscopy and Theoretical Calculations.

A systematic study of the structures and electronic properties of V2-doped silicon clusters, V2Sin-/0 (n = 7-14), was carried out by anion photoelectron spectroscopic experiments combined with theoretical calculations. According to the experimental spectra of V2Sin- (n = 7-14) clusters, the V2Si12- cluster has the highest vertical detachment energy (VDE) of 3.66 eV, while V2Si7- and V2Si14- clusters have lower VDEs of 2.81 and 2.84 eV, respectively. The most stable structure searches find that two V atoms in the V2Sin- clusters with size n = 7 and 8 are located at the surface, while V2Sin- clusters with n ≥ 9 prefer cage-like structures. Based on the analysis of the structural evolution of V2Sin- (n = 9-14) clusters, it can be clearly seen how the antihexagonal prism with one V encapsulated in the cage is gradually built from n = 9 to 12 and further developed from n = 12 to 14 with the extra silicon atoms located at the surface of the Si12 cage. The molecular orbital and the atoms in molecule analysis of the V2Sin- (n = 7-14) anions demonstrate that the strong V-V bond and the delocalized interaction between the V2 moiety and the Sin ligand play a significant role in stabilizing the cluster structures. A strong linear correlation has been found between the Wiberg bond order of the V-V bond and the electron density at the V-V bond critical points.

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.

CAM-NAS: An Efficient and Interpretable Neural Architecture Search Model Based on Class Activation Mapping

Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network architecture search (NAS) serves as a solution to help experts discover new network structures that are suitable for different tasks and platforms. However, traditional NAS algorithms often consume time and many computational resources, especially when dealing with complex tasks and large-scale models, and the search process can become exceptionally time-consuming and difficult to interpret. In this paper, we propose a class activation graph-based neural structure search method (CAM-NAS) to address these problems. Compared with traditional NAS algorithms, CAM-NAS does not require full training of submodels, which greatly improves the search efficiency. Meanwhile, CAM-NAS uses the class activation graph technique, which makes the searched models have better interpretability. In our experiments, we tested CAM-NAS on an NVIDIA RTX 3090 graphics card and showed that it can evaluate a submodel in only 0.08 seconds, which is much faster than traditional NAS methods. In this study, we experimentally evaluated CAM-NAS using the CIFAR-10 and CIFAR-100 datasets as benchmarks. The experimental results show that CAM-NAS achieves very good results. This not only proves the efficiency of CAM-NAS, but also demonstrates its powerful performance in image classification tasks.

Interception of chain shape Ce nitrides at ambient conditions

A high-pressure study of the CeN5 compounds is performed by the crystal structure search method in combination with the first principle calculations. Four new phases C2-CeN5, P-1-CeN5, P2/c-CeN5, and P21/m-CeN5 were predicted at 0, 20, 50, and 100 GPa, respectively. The analysis of stability shows that C2-CeN5, P-1-CeN5, and P21/m-CeN5 can be quenched to ambient conditions. The stabilization mechanism of Ce nitrides is clarified by the analysis of electronic structures. The phase transformation of CeN5 with the pressure is revealed by the enthalpy difference analysis. P2/c-CeN5 and P21/m-CeN5 with polymeric N-chains not only exhibit high energy densities (8.67 and 9.37 kJ/cm3), but also outstanding detonation velocities (11.43 and 12.10 km/s) and detonation pressures (90.39 and 102.82 GPa).

Crystal structures and P-T phase diagrams of SrC O and BaC O .

In this study, we present the results of a search for new stable structures of SrC O and BaC O in the pressure range of 0-100 GPa based on the density functional theory and crystal structure prediction approaches. We have shown that the recently synthesized pyrocarbonate structure SrC O - is thermodynamically stable for both SrC O and BaC O . Thus, SrC O - is stable relative to decomposition reaction above 10 GPa, while the lower-pressure stability limit for BaC O - is 5 GPa, which is the lowest value for the formation of pyrocarbonates. For SrC O , the following polymorphic transitions were found with increasing pressure: at 40 GPa and 1000 K, at 90 GPa and 1000 K. SrC O - and SrC O - are characterized by the framework and layered structures of [CO ] tetrahedra, respectively. For BaC O , with increasing pressure, decomposition of BaC O - into BaCO and CO is observed at 34 GPa without any polymorphic transitions.

Exploration of the crystal structures and electronic features of Y2O3:Er and Y2O3:Er,Yb crystals: a first-principles study

The Er3+ doped Y2O3 (Y2O3:Er) and Er3+, Yb3+ co-doped Y2O3 (Y2O3:Er,Yb) have attracted considerable attention due to their outstanding optical properties. However, the crystal structures underlying their electronic features are still unclear. In this paper, we carried out the systematical structural evolutions of Y2O3:Er and Y2O3:Er,Yb via the Crystal Structural AnaLYsis by Particle Swarm Optimization structure search method. Two novel monoclinic structures with P2 space group were uncovered for Y2O3:Er and Y2O3:Er,Yb. Validities of the two ground state structures were verified by the good agreements between the calculated x-ray diffraction patterns and the experimental ones. Phonon dispersion curves were also investigated, and the data showed that the ground state structures of both Y2O3:Er and Y2O3:Er,Yb were dynamically stable. Moreover, the density of states, the band structure and the electronic local function of these two systems were also studied. The impurity Er3+ and Yb3+ ions lead to the transition of electronic feature from the insulator to the semiconductor. The Y–O, Er–O, Yb–O bonds are proved to be covalent bonds. These research results can fill up the vacancy and give instructions in the rare-earth doped laser materials in the future.

Decoding Chemical Formula to Spatial Conformation: A Structural Study Targeting the [Au25(SR)19]0 Nanocluster.

Structural global searches employing highly efficient algorithms have been extensively applied for studying molecules and clusters. However, the code-aided spatial conformational determination of thiolated gold nanoclusters (AuNCs) has not been accomplished because of the complex structural architecture of AuNCs, especially when only the chemical formula of the cluster is known. Experiments have shown that the star [Au25(SR)18]-1 cluster can transform into the [Au25(SR)19]0 cluster. However, the crystal structure of the [Au25(SR)19]0 cluster has not been experimentally determined, and theoretical structural predictions for this cluster are challenging because no template cluster presents for [Au25(SR)19]0. Utilizing the grand unified model, this study succeeded in obtaining the structure of the [Au25(SR)19]0 cluster by using minimal computations, which was verified to be reasonable through stability analysis and experimental absorption spectrum confirmation. Although the predicted [Au25(SR)19]0 cluster has the same number of Au atoms as the [Au25(SR)18]-1 cluster, the structure is considerably altered, owing to the presence of a face-centered cubic kernel. This study provides insights for decoding the chemical formulas of AuNCs to determine their spatial conformations.

Rh19-: A high-spin super-octahedron cluster.

Probing atomic clusters with magic numbers is of supreme importance but challenging in cluster science. Pronounced stability of a metal cluster often arises from coincident geometric and electronic shell closures. However, transition metal clusters do not simply abide by this constraint. Here, we report the finding of a magic-number cluster Rh19- with prominent inertness in the sufficient gas-collision reactions. Photoelectron spectroscopy experiments and global-minimum structure search have determined the geometry of Rh19- to be a regular Oh‑[Rh@Rh12@Rh6]- with unusual high-spin electronic configuration. The distinct stability of such a strongly magnetic cluster Rh19- consisting of a nonmagnetic element is fully unveiled on the basis of its unique bonding nature and superatomic states. The 1-nanometer-sized Oh-Rh19- cluster corresponds to a fragment of the face-centered cubic lattice of bulk rhodium but with altered magnetism and electronic property. This cluster features exceptional electron-spin state isomers confirmed in photoelectron spectra and suggests potential applications in atomically precise manufacturing involving spintronics and quantum computing.

Use of carbon-13 NMR to identify known natural products by querying a nuclear magnetic resonance database-An assessment.

The quick identification of known organic low molecular weight compounds, also known as structural dereplication, is a highly important task in the chemical profiling of natural resource extracts. To that end, a method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called acd_lotus, was reported earlier, and its usefulness was illustrated by only three examples. This article presents the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of the biological magnetic resonance bank (BMRB). Two compound retrieval methods were employed. The first one involves searching in the acd_lotus database using commercial software. The second one operates through the freely accessible web interface of the nmrshiftdb2 database, which includes the compounds present in acd_lotus and many others. The two structural dereplication methods have proved to be efficient and can be used together in a complementary way.

Growth pattern and electronic and magnetic properties of Cr-doped silver clusters.

Growth pattern and electronic and magnetic properties of Agn Cr (n = 1-16) clusters have been investigated via density functional theory (DFT) combined with CALYPSO structure search method. The optimized geometry shows that the growth of the global minimum structures of Agn Cr clusters have obvious rule. when n > 12, silver atoms grow around an icosahedron which is almost unchanged in each structure. Analyses of electronic properties indicate that the doped Cr atom can only enhance the stability of larger silver clusters. Optical absorption and photoelectron spectra of Agn Cr isomers have been predicted and can be used for their structural identification. The icosahedral Ag12 Cr cluster with large energy level gap can be seen as a superatom. The adsorption capacity of Cr atom in Agn Cr cluster to CO is much higher than that of free Cr atom. The intensity of IR and Ramam spectra can be dramatically enhanced when CO is absorbed on Agn Cr cluster that Cr atom is encapsulated by Ag atoms. Moreover, the red shift of IR and Raman spectra of CO adsorbed on these clusters is also very small compared to free CO. Magnetism calculations show that the magnetic moment of Agn Cr clusters decreases linearly from n = 6 to 12 and increases linearly from n = 12 to 16. The total magnetic moment of Agn Cr cluster is mainly localized on the Cr atom. The change of magnetic moment of Cr atom is related to the charge transfer between Cr and Ag atoms.

Crystal structure search with principal invariants

In this study, we propose a general way of finding a new crystal structure starting from a database. We also introduce a concise representation of the data based on principal invariants derived from the spatial arrangement of atoms in the crystal structure. Since the selection of the present representation does not depend on the size and selection method of the unit-cell defining the crystal structure, it is ideal for measuring similarity between two arbitrarily different crystal structures. For a more general search for crystal structures, the known crystal structure space is better defined using a variational autoencoder. Furthermore, we used either Pearson's correlation or the square root of the Jensen-Shannon divergence to directly search similar crystal structures for a wide variety of space groups. Our novel method uses extended database information, the similarity measure, and first-principles electronic structure calculation to predict several new metastable crystal structures of SnSe, P, and SnS.

Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials.

Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged in experimental materials databases. However, there still exist cation combinations with unknown stability and structures of their compounds in oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary metal oxide systems, encompassing most cations except for partially filled 3d or f shells, and determine their lowest-energy crystal structures with representative stoichiometry derived from prevalent oxidation states or recommender systems. Consequently, we discover 45 ternary oxide systems containing stable compounds against decomposition into binary or elemental phases, the majority of which incorporate noble metals. Comparisons with other theoretical databases highlight the strengths and limitations of informatics-based material searches. With a relatively modest computational resource requirement, we contend that heuristic-based structure searches, as demonstrated in this study, offer a promising approach for future materials discovery endeavors.

Polynitrogen clusters interaction with water: experimental and theoretical perspectives

Polynitrogen clusters are of great interest as potential high-energy-density materials (HEDMs). However, it is still a challenge to effectively manufacture pure nitrogen clusters. Here, we present a method to produce all-nitrogen clusters ranging in size from 3 to 10 using a self-developed apparatus consisting of a laser vaporization cluster source and a mass spectrometer. The results indicate that the mass peak of the N4 + cluster is dominant, and the cooling of the cluster source with liquid nitrogen effectively enhanced the intensities of larger nitrogen clusters. The trace amounts of water in the carrier gas affects the relative signal intensities of nitrogen clusters, suggesting that water molecules can affect their stabilities. The geometrical structures of the N(n = 3–10) clusters are determined by CALYPSO cluster structural search method and density functional theory calculations. A series of promising polynitrogen clusters, such as Z-shaped N clusters and bicyclic N clusters, are identified. The molecular dynamics simulations indicate the decomposition energy barrier of (N4–H2O)+ cluster is lower than that of N4 + cluster by 2.3 kcal mol−1, while the decomposition energy barrier of (N8–H2O)+ cluster is higher than that of N8 + cluster by 1.4 kcal mol−1. These findings provide useful information for the generation and stabilization of polynitrogen clusters, which are valuable for the design and synthesis of HEDMs.

In Silico Discovery of Potential Inhibitors Targeting the RNA Binding Loop of ADAR2 and 5-HT2CR from Traditional Chinese Natural Compounds.

Adenosine deaminase acting on RNA 2 (ADAR2) is an important enzyme involved in RNA editing processes, particularly in the conversion of adenosine to inosine in RNA molecules. Dysregulation of ADAR2 activity has been implicated in various diseases, including neurological disorders (including schizophrenia), inflammatory disorders, viral infections, and cancers. Therefore, targeting ADAR2 with small molecules presents a promising therapeutic strategy for modulating RNA editing and potentially treating associated pathologies. However, there are limited compounds that effectively inhibit ADAR2 reactions. This study therefore employed computational approaches to virtually screen natural compounds from the traditional Chinese medicine (TCM) library. The shortlisted compounds demonstrated a stronger binding affinity to the ADAR2 (<-9.5 kcal/mol) than the known inhibitor, 8-azanebularine (-6.8 kcal/mol). The topmost compounds were also observed to possess high binding affinity towards 5-HT2CR with binding energies ranging from -7.8 to -12.9 kcal/mol. Further subjecting the top ADAR2-ligand complexes to molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations revealed that five potential hit compounds comprising ZINC000014637370, ZINC000085593577, ZINC000042890265, ZINC000039183320, and ZINC000101100339 had favorable binding free energies of -174.911, -137.369, -117.236, -67.023, and -64.913 kJ/mol, respectively, with the human ADAR2 protein. Residues Lys350, Cys377, Glu396, Cys451, Arg455, Ser486, Gln488, and Arg510 were also predicted to be crucial in ligand recognition and binding. This finding will provide valuable insights into the molecular interactions between ADAR2 and small molecules, aiding in the design of future ADAR2 inhibitors with potential therapeutic applications. The potential lead compounds were also profiled to have insignificant toxicities. A structural similarity search via DrugBank revealed that ZINC000039183320 and ZINC000014637370 were similar to naringin and naringenin, which are known adenosine deaminase (ADA) inhibitors. These potential novel ADAR2 inhibitors identified herein may be beneficial in treating several neurological disorders, cancers, viral infections, and inflammatory disorders caused by ADAR2 after experimental validation.

Crystal system and space group prediction of two-dimensional materials from chemical formula via deep neural networks

Predictive modeling of undiscovered two-dimensional (2D) materials plays an important role in 2D materials research. In the initial stage of modeling, predicting the crystal system and space group, two fundamental symmetry parameters of a crystal, from their chemical formula is essential for global structure search and discovery of new 2D crystalline materials. Here, we develop a deep neural network (DNN) model containing 290 descriptors to predict the crystal system and space group of 2D materials in AA stacking based on their chemical formulas. Our model achieves an accuracy of 74.56% in predicting the crystal system and 73.14% in predicting the space group with significant generality. In addition, the probability of obtaining the correct space group from the top five candidates reaches 89.58%. Our results also demonstrate that approximate predictions can be generated with the top 80 descriptors with the predicted results close to those obtained with all 290 descriptors, indicating the relative feature importance difference of descriptors. This study provides an effective DNN model for the quick determination of crystal systems and space groups for the global structure search of 2D materials.

Deep Learning-based structure modelling illuminates structure and function in uncharted regions of β-solenoid fold space

Repeat proteins are common in all domains of life and exhibit a wide range of functions. One class of repeat protein contains solenoid folds where the repeating unit consists of β-strands separated by tight turns. β-solenoids have distinguishing structural features such as handedness, twist, oligomerisation state, coil shape and size which give rise to their diversity. Characterised β-solenoid repeat proteins are known to form regions in bacterial and viral virulence factors, antifreeze proteins and functional amyloids. For many of these proteins, the experimental structure has not been solved, as they are difficult to crystallise or model. Here we use various deep learning-based structure-modelling methods to discover novel predicted β-solenoids, perform structural database searches to mine further structural neighbours and relate their predicted structure to possible functions. We find both eukaryotic and prokaryotic adhesins, confirming a known functional linkage between adhesin function and the β-solenoid fold. We further identify exceptionally long, flat β-solenoid folds as possible structures of mucin tandem repeat regions and unprecedentedly small β-solenoid structures. Additionally, we characterise a novel β-solenoid coil shape, the FapC Greek key β-solenoid as well as plausible complexes between it and other proteins involved in Pseudomonas functional amyloid fibres.

Research on power system fault prediction based on GA-CNN-BiGRU

Introduction: This paper proposes a power system fault prediction method that utilizes a GA-CNN-BiGRU model. The model combines a genetic algorithm (GA), a convolutional neural network (CNN), and a bi-directional gated recurrent unit network Bidirectional Gated Recurrent Unit to accurately predict and analyze power system faults.Methods: The proposed model employs a genetic algorithm for structural search and parameter tuning, optimizing the model structure. The CNN is used for feature extraction, while the bi-directional gated recurrent unit network is used for sequence modeling. This approach captures the correlations and dependencies in time series data and effectively improves the prediction accuracy and generalization ability of the model.Results and Discussion: Experimental validation shows that the proposed method outperforms traditional and other deep learning-based methods on multiple data sets in terms of prediction accuracy and generalization ability. The method can effectively predict and analyze power system faults, providing crucial support and aid for the operation and management of power systems.

Introducing AI: The Search for Structure in Managing a General Purpose Technology

Introducing AI: The Search for Structure in Managing a General Purpose Technology