Prediction of gas-phase reduced ion mobility constants (K0) from three-dimensional molecular structure representation

2012 ◽  
Vol 90 (8) ◽  
pp. 640-651
Author(s):  
Jing Song ◽  
Ying Zhang ◽  
Hui Hu ◽  
Hui Zhang ◽  
Lin Lin ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Gas Phase ◽  
Ion Mobility ◽  
Linear Models ◽  
Three Dimensional ◽  
Structural Features ◽  
Structure Property ◽  
Ann Model ◽  
Test Set ◽  
Structure Representation

Quantitative structure–property relationship (QSPR) studies were performed for the prediction of gas-phase reduced ion mobility constants (K0) of diverse compounds based on three-dimensional (3D) molecular structure representation. The entire set of 159 compounds was divided into a training set of 120 compounds and a test set of 39 compounds according to Kennard and Stones algorithm. Multiple linear regression (MLR) analysis was employed to select the best subset of descriptors and to build linear models, whereas nonlinear models were developed by means of an artificial neural network (ANN). The obtained models with five descriptors involved show good predictive power for the test set: a squared correlation coefficient (R2) of 0.9029 and a standard error of estimation (s) of 0.0549 were achieved by the MLR model, whereas by the ANN model, R2 and s were 0.9292 and 0.496, respectively. The results of this study compare favorably to previously reported prediction methods for the ion mobility constants. In addition, the descriptors used in the models are discussed with respect to the structural features governing the mobility of the compounds.

Prediction of the maximum nonseizure load of lubricant additives

2017 ◽  
Vol 16 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Xinliang Yu ◽  
Rimeng Zhan ◽  
Jiyong Deng ◽  
Xianwei Huang
Keyword(s):  
Lubricant Additives ◽  
Structure Property ◽  
Ann Model ◽  
Training Set ◽  
Qspr Model ◽  
Test Set ◽  
Base Oil ◽  
Chemical Structures ◽  
Artificial Neural Network Ann ◽  
Backward Propagation

Lubricating additives can improve the lubricant performance of base oil in reducing friction and wear and minimizing loss of energy. It is of great significance to study the relationship between chemical structures and lubrication properties of lubricant additives. This paper reports a quantitative structure–property relationship (QSPR) model of the maximum nonseizure loads ([Formula: see text]) of 79 lubricant additives by applying artificial neural network (ANN) based on the algorithm of backward propagation of errors. Six molecular descriptors appearing in the multiple linear regression (MLR) model were used as vectors to develop the ANN model. The optimal condition of ANN with network structure of [6-4-1] was obtained by adjusting various parameters by trial-and-error. The root-mean-square (rms) errors from ANN model are [Formula: see text] ([Formula: see text]) for the training set and [Formula: see text] ([Formula: see text]) for the test set, which are superior to the MLR results of [Formula: see text] ([Formula: see text]) for the training set and [Formula: see text] ([Formula: see text]) for the test set. Compared to the existing model for [Formula: see text], our model has better statistical quality. The results indicate that our ANN model can be applied to predict the [Formula: see text] values for lubricant additives.

scholarly journals Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

2018 ◽  
Author(s):  
Shuangjia Zheng ◽  
Xin Yan ◽  
Yuedong Yang ◽  
Jun Xu
Keyword(s):  
Molecular Structure ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Data Sets ◽  
Structure Property ◽  
Structure Representation ◽  
Syntax Analysis ◽  
Structure Property Relationships ◽  
Connection Table ◽  
Structure Property Relationship

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

scholarly journals Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3271 ◽  
Author(s):  
Imane Naboulsi ◽  
Aziz Aboulmouhajir ◽  
Lamfeddal Kouisni ◽  
Faouzi Bekkaoui ◽  
Abdelaziz Yasri
Keyword(s):  
Tyrosine Kinases ◽  
Kinase Inhibitors ◽  
Structural Features ◽  
Ann Model ◽  
Training Set ◽  
Test Set ◽  
Lyn Kinase ◽  
Physico Chemical ◽  
The Mean ◽  
Artificial Neural Network Ann

Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors.

scholarly journals Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

2018 ◽  
Author(s):  
Shuangjia Zheng ◽  
Xin Yan ◽  
Yuedong Yang ◽  
Jun Xu
Keyword(s):  
Molecular Structure ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Data Sets ◽  
Structure Property ◽  
Structure Representation ◽  
Syntax Analysis ◽  
Structure Property Relationships ◽  
Connection Table ◽  
Structure Property Relationship

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

Effects of electrospray mechanisms and structural relaxation on polylactide ion conformations in the gas phase: insights from ion mobility spectrometry and molecular dynamics simulations

2020 ◽  
Vol 22 (7) ◽  
pp. 4193-4204 ◽  
Author(s):  
Quentin Duez ◽  
Haidy Metwally ◽  
Sébastien Hoyas ◽  
Vincent Lemaur ◽  
Jérôme Cornil ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Gas Phase ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Structural Relaxation ◽  
Structural Features ◽  
Formation Mechanisms ◽  
Dynamics Simulations

Gas-phase polymer ions may retain structural features associated with their electrospray formation mechanisms.

The structure of the 1H-imidazol-3-ium lawsonate salt aided byab initiogas-phase calculations

2013 ◽  
Vol 69 (4) ◽  
pp. 403-406
Author(s):  
Marcos Antônio Ribeiro ◽  
Willian Xerxes Coelho Oliveira ◽  
Humberto Osório Stumpf ◽  
Carlos Basílio Pinheiro
Keyword(s):  
Hydrogen Bond ◽  
Ab Initio ◽  
Gas Phase ◽  
Three Dimensional ◽  
Structural Features ◽  
Organic Salt ◽  
Phase Structures ◽  
Different Types

For the new organic salt 1H-imidazol-3-ium 1,4-dioxo-1,4-dihydronaphthalen-2-olate, C3H5N2+·C10H5O3−,ab initiocalculations of the gas-phase structures of the lawsonate and imidazolium ions were performed to help in the interpretation of the structural features observed. Three different types of hydrogen bond are responsible for the three-dimensional packing of the salt.

scholarly journals Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

2018 ◽  
Author(s):  
Shuangjia Zheng ◽  
Xin Yan ◽  
Yuedong Yang ◽  
Jun Xu
Keyword(s):  
Molecular Structure ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Data Sets ◽  
Structure Property ◽  
Structure Representation ◽  
Syntax Analysis ◽  
Structure Property Relationships ◽  
Connection Table ◽  
Structure Property Relationship

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

Methods for making stereoslides and -prints, with freeze-etched olfactory epithelium as example

1984 ◽  
Vol 42 ◽  
pp. 704-705
Author(s):  
Bert Ph. M. Menco ◽  
Ido F. Menco ◽  
Frans L.T. Verdonk
Keyword(s):  
Electron Microscope ◽  
Olfactory Epithelium ◽  
Three Dimensional ◽  
Supporting Cell ◽  
Structural Features ◽  
Extensive Study ◽  
Sprague Dawley ◽  
Freezing Method ◽  
Respiratory Epithelia ◽  
Three Dimensional Presentation

Previously we presented an extensive study of the distributions of intramembranous particles of structures in apical surfaces of nasal olfactory and respiratory epithelia of the Sprague-Dawley rat. For the same structures these distributions were compared in samples which were i) chemically fixed and cryo-protected with glycerol before cryo-fixation, after excision, and ii)ultra-rapidly frozen by means of the slam-freezing method. Since a three-dimensional presentation markedly improves visualization of structural features micrographs were presented as stereopairs. Two exposures were made by tiling the sample stage of the electron microscope 6° in either direction with an eucentric goniometer. The negatives (Agfa Pan 25 Professional) were reversed with Kodak Technical Pan Film 2415 developed in D76 1:1. The prints were made from these reversed negatives. As an example tight-junctional features of an olfactory supporting cell in a region where this cell conjoined with two other cells are presented (Fig. 1).

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