APPLICATIONS OF A NEW TOPOLOGICAL INDEX EDm IN SOME ALIPHATIC HYDROCARBONS

2009 ◽  
Vol 08 (01) ◽  
pp. 19-45 ◽  
Author(s):  
CHANGMING NIE ◽  
YAXIN WU ◽  
RONGYAN WU ◽  
SAIHONG JIANG ◽  
CONGYI ZHOU
Keyword(s):  
Structural Information ◽  
Correlation Coefficients ◽  
Distance Matrix ◽  
Molar Refraction ◽  
Molecular Structures ◽  
Topological Descriptors ◽  
Standard Enthalpy Of Formation ◽  
Standard Entropy ◽  
Structure Property ◽  
Index Matrix

A novel index EDm based on ionicity index matrix, improved distance matrix, and branching degree matrix is used to describe the structural information of the molecule and realizes unique characterization of the molecular structures. The quantitative structure–property relationship (QSPR) models, with correlation coefficients (R) in the range of 0.99–1.00 for standard enthalpy of formation ([Formula: see text]), standard entropy ([Formula: see text]), molar volume (V m ), and molar refraction (R m ) of alkanes, alkenes, and alkynes, are subsequently developed using the index EDm. The leave-one-out (LOO) method and random sampling prediction (RSP) method demonstrate the models to be statistically significant and reliable. Compared with other published topological descriptors, the index EDm has many advantages such as zero degeneracy, better simulation, and so on. Furthermore, the models of solubility and octanol–water partition coefficient are built with satisfied results, which further manifests the superiority and wide application of this index.

QSPR/QSAR Study of Mercaptans by Quantum Topological Method

2011 ◽  
Vol 233-235 ◽  
pp. 2536-2540
Author(s):  
Xuan Chen ◽  
Chang Ming Nie ◽  
Song Nian Wen
Keyword(s):  
Density Functional ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Molecular Structures ◽  
Structure Property ◽  
Qsar Study ◽  
Validation Parameters ◽  
The Stability ◽  
Leave One Out

A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

Probing vacancies and structural distortions at individual defects in ceramics using EELS

1996 ◽  
Vol 54 ◽  
pp. 678-679
Author(s):  
D. J. Wallis ◽  
N. D. Browning
Keyword(s):  
Structural Information ◽  
Core Loss ◽  
Nearest Neighbors ◽  
Ceramic Materials ◽  
Scanning Transmission Electron Microscope ◽  
Real Space ◽  
Atomic Scale ◽  
Structure Property ◽  
Scanning Transmission ◽  
Key Issues

In electron energy loss spectroscopy (EELS), the near-edge region of a core-loss edge contains information on high-order atomic correlations. These correlations give details of the 3-D atomic structure which can be elucidated using multiple-scattering (MS) theory. MS calculations use real space clusters making them ideal for use in low-symmetry systems such as defects and interfaces. When coupled with the atomic spatial resolution capabilities of the scanning transmission electron microscope (STEM), there therefore exists the ability to obtain 3-D structural information from individual atomic scale structures. For ceramic materials where the structure-property relationships are dominated by defects and interfaces, this methodology can provide unique information on key issues such as like-ion repulsion and the presence of vacancies, impurities and structural distortion.An example of the use of MS-theory is shown in fig 1, where an experimental oxygen K-edge from SrTiO3 is compared to full MS-calculations for successive shells (a shell consists of neighboring atoms, so that 1 shell includes only nearest neighbors, 2 shells includes first and second-nearest neighbors, and so on).

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

scholarly journals On detour index of cycloparaphenylene and polyphenylene molecular structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Prabhu ◽  
Y. Sherlin Nisha ◽  
M. Arulperumjothi ◽  
D. Sagaya Rani Jeba ◽  
V. Manimozhi
Keyword(s):  
Atomic System ◽  
Molecular Structures ◽  
Topological Descriptors ◽  
Covalent Bonds ◽  
Atomic Structures ◽  
Topological Descriptor ◽  
Structure Activity ◽  
Mathematical Correlation ◽  
Benzene Rings ◽  
Unique Method

AbstractCycloparaphenylene is a particle that comprises a few benzene rings associated with covalent bonds in the para positions to frame a ring-like structure. Similarly, poly (para-phenylenes) are macromolecules that include benzenoid compounds straightforwardly joined to each other by C–C bonds. Because of their remarkable architectural highlights, these structures have fascinated attention from numerous vantage focuses. Descriptors are among the most fundamental segments of prescient quantitative structure-activity and property relationship (QSAR/QSPR) demonstrating examination. They encode chemical data of particles as quantitative numbers, which are utilized to create a mathematical correlation. The nature of a predictive model relies upon great demonstrating insights, yet additionally on the extraction of compound highlights. To a great extent, Molecular topology has exhibited its adequacy in portraying sub-atomic structures and anticipating their properties. It follows a two-dimensional methodology, just thinking about the interior plan, including molecules. Explicit subsets speak the design of every atom of topological descriptors. When all around picked, these descriptors give a unique method of describing an atomic system that can represent the most significant highlights of the molecular structure. Detour index is one such topological descriptor with much application in chemistry, especially in QSAR/QSPR studies. This article presents an exact analytical expression for the detour index of cycloparaphenylene and poly (para-phenylene).

scholarly journals Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1788
Author(s):  
Vy T. Duong ◽  
Elizabeth M. Diessner ◽  
Gianmarc Grazioli ◽  
Rachel W. Martin ◽  
Carter T. Butts
Keyword(s):  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Dynamic Models ◽  
Structural Information ◽  
Coarse Graining ◽  
Molecular Structures ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Scalable Network ◽  
Atomic Coordinates

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design

2012 ◽  
Vol 1425 ◽  
Author(s):  
Michael P. Krein ◽  
Bharath Natarajan ◽  
Linda S. Schadler ◽  
L. C. Brinson ◽  
Hua Deng ◽  
...  
Keyword(s):  
A Priori ◽  
Molecular Structures ◽  
Work Of Adhesion ◽  
Materials Informatics ◽  
Structure Property ◽  
Surface Tensions ◽  
Virtual Experiments ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Informatics Tools

ABSTRACTPolymer nanocomposites (PNC) are complex material systems in which the dominant length scales converge. Our approach to understanding nanocomposite tradespace uses Materials Quantitative Structure-Property Relationships (MQSPRs) to relate molecular structures to the polar and dispersive components of corresponding surface tensions. If the polar and dispersive components of surface tensions in the nanofiller and polymer could be determined a priori, then the propensity to aggregate and the change in polymer mobility near the particle could be predicted. Derived energetic parameters such as work of adhesion, work of spreading and the equilibrium wetting angle may then used as input to continuum mechanics approaches that have been shown able to predict the thermomechanical response of nanocomposites and that have been validated by experiment. The informatics approach developed in this work thus enables future in silico nanocomposite design by enabling virtual experiments to be performed on proposed nanocomposite compositions prior to fabrication and testing.

scholarly journals An Improved QSPR Modeling of Hydrocarbon Dipole Moments

2004 ◽  
Vol 4 ◽  
pp. 956-964
Author(s):  
Igor V. Nesterov ◽  
Andrey A. Toropov ◽  
Pablo R. Duchowicz ◽  
Eduardo A. Castro
Keyword(s):  
Regression Analysis ◽  
Dipole Moments ◽  
Distance Matrix ◽  
Basic Number ◽  
Topological Descriptors ◽  
Multivariable Regression Analysis ◽  
Qspr Modeling ◽  
Multivariable Regression ◽  
Electrotopological State

Dipole moments of hydrocarbons are not an easy property to model with conventional 2D descriptors. A comparison of the performance of the most commonly used sets of topological descriptors is presented, each set containing descriptors derived from the regular and Detour distance matrix, Electrotopological State Indices, and the basic number of atoms of each type and bonds. Data were taken on a representative set of 35 hydrocarbon dipole moments previously reported and the classical multivariable regression analysis for establishing the models is employed.

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