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 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.

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.

QSPR Study on n-Octanol/Water Partition Coefficient of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field

2011 ◽  
Vol 356-360 ◽  
pp. 83-88 ◽  
Author(s):  
Shu Qiao ◽  
Kun Xie ◽  
Chuan Fu ◽  
Jie Pan
Keyword(s):  
Partition Coefficient ◽  
Organic Pollutants ◽  
Three Dimensional ◽  
Qsar Model ◽  
Atomic Interaction ◽  
Structure Property ◽  
Qspr Model ◽  
Chemical Structures ◽  
Interaction Field ◽  
Water Partition Coefficient

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are a group of important persistent organic pollutants. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, a QSPR model is established for estimating n-octanol/water partition coefficient (log KOW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, SMR-PLS QSAR model has been created and good correlation coefficients and cross-validated correlation coefficient is obtained. Predictive capability of the models has also been demonstrated by leave-one-out cross-validation. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

2018 ◽  
Vol 3 (1) ◽  
pp. 119-133 ◽  
Author(s):  
B. Elidrissi ◽  
A. Ousaa ◽  
M. Ghamali ◽  
S. Chtita ◽  
M. A. Ajana ◽  
...  
Keyword(s):  
Density Functional ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Ann Model ◽  
Qsar Study ◽  
Test Set ◽  
Physico Chemical ◽  
Pca Method ◽  
Artificial Neural Network Ann ◽  
Hiv 1

A Quantitative Structure–Activity Relationship (QSAR) study was performed to predict HIV-1 integrase inhibition activity (pIC50) of thirty-five 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4-carboxamide compounds using the electronic and physico-chemical descriptors computed respectively, with Gaussian 03W and ACD/ChemSketch programs. The structures of all compounds were optimized using the hybrid Density Functional Theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 28 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the principal components analysis (PCA) method, the descendant Multiple Linear Regression (MLR) analyses and the Artificial Neural Network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through a test set. This study shows that the MLR has served marginally better to predict pIC50 activity, when compared with the results given by predictions made with a (4-3-1) ANN model.

QSAR Studies on the IC50 of A Class of Thiazolidinone/ Thiazolide Based Hybrids as Antitrypanosomal Agents

2020 ◽  
Vol 17 ◽  
Author(s):  
Bo Yang ◽  
Hongzong Si ◽  
Honglin Zhai
Keyword(s):  
Gene Expression ◽  
Negative Impact ◽  
Gene Expression Programming ◽  
Heuristic Method ◽  
Relative Number ◽  
Programming Method ◽  
Positive Contribution ◽  
Training Set ◽  
Test Set ◽  
Chemical Structures

Background:: Trypanosomiasis is a widespread zoonotic disease. Existing drugs are not enough to prevent and treat it. Objective:: This study aimed to build a quantitative structure-activity relationship model by the chemical structures of a class of thiazolidone/thiazolidamide based hybrids. The model was used to screen new antitrypanosomal agents and predict the properties of composite molecules. Methods:: All compounds were randomly divided into a training set and a test set. A large number of descriptors were calculated by the software, then some of the best descriptors were selected to build the models. The linear model was built by the heuristic method and the nonlinear model was built by gene expression programming method. Results and Discussion:: In the heuristic method, the correlation coefficients , , and were 0.581, 0.457, 14.053 and 15.311, respectively. In gene expression programming, the and were 0.715, 10.997 in the training set and 0.617, 22.778 in the test set. The results showed that the relative number of S atoms and the minimum bond order of an H atom had a significant positive contribution to IC50. Meanwhile, the relative number of double bonds and the count of hydrogen-bonding acceptor sites had a great negative impact on IC50. Conclusion:: Both the heuristic method and gene expression programming had a good predictive performance. By contrast, gene expression programming method fitted well with the experimental values and it was expected to be beneficial in the synthesis of new antitrypanosomal drugs.

Investigation of Neural-Network-Based Inverse Kinematics for a 6-DOF Serial Manipulator With Non-Spherical Wrist

2018 ◽  
Author(s):  
Benjamin E. Hargis ◽  
Wesley A. Demirjian ◽  
Matthew W. Powelson ◽  
Stephen L. Canfield
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Computational Time ◽  
Robot Arm ◽  
Training Set ◽  
Test Set ◽  
Network Training ◽  
Inverse Kinematics Problem ◽  
Spherical Wrist ◽  
Artificial Neural Network Ann

This study proposes using an Artificial Neural Network (ANN) to train a 6-DOF serial manipulator with a non-spherical wrist to solve the inverse kinematics problem. In this approach, an ANN has been trained to determine the configuration parameters of a serial manipulator that correspond to the position and pose of its end effector. The network was modeled after the AUBO-i5 robot arm, and the experimental results have shown the ability to achieve millimeter accuracy in tool space position with significantly reduced computational time relative to an iterative kinematic solution when applied to a subset of the workspace. Furthermore, a separate investigation was conducted to quantify the relationship between training example density, training set error, and test set error. Testing indicates that, for a given network, sufficient example point density may be approximated by comparing the training set error with test set error. The neural network training was performed using the MATLAB Neural Network Toolbox.

Prediction of Detonation Velocity of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 790 ◽  
pp. 673-676
Author(s):  
Yue Qiang Jiang ◽  
Yong Gang Liu ◽  
Xin Tian ◽  
Gong Bing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Velocity ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network (ANN) model was constructed to predict the detonation velocity of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation velocity was used as output. The dataset of 61 aluminized explosives was randomly divided into a training set (49) and a prediction set (12). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [812, calculated detonation velocity show good agreement with experimental results. It is shown that ANN is able to produce accurate predictions of the detonation velocity of aluminized explosive.

Experimental analysis and modeling of viscosity and thermal conductivity of GNPs/SAE 5W40 nanolubricant

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valéry Tusambila Wadi ◽  
Özkan Özmen ◽  
Mehmet Baki Karamış
Keyword(s):  
Thermal Conductivity ◽  
Peer Review ◽  
Dynamic Viscosity ◽  
Ann Model ◽  
Step Method ◽  
Rotational Viscometer ◽  
Content Type ◽  
Base Oil ◽  
Artificial Neural Network Ann ◽  
The Stability

Purpose The purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant. Design/methodology/approach Nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity. Findings The designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations. Practical implications This study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils. Originality/value To the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/

QSPR Study on Aqueous Solubility of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field

2011 ◽  
Vol 356-360 ◽  
pp. 95-100
Author(s):  
Kun Xie ◽  
Shu Qiao ◽  
Chuan Fu ◽  
Cong Cheng
Keyword(s):  
Three Dimensional ◽  
Aqueous Solubility ◽  
Partial Least Square ◽  
Least Square ◽  
Atomic Interaction ◽  
Structure Property ◽  
Qspr Model ◽  
Chemical Structures ◽  
Interaction Field ◽  
Leave One Out

A quantitative structure property relationship (QSPR) model is established for estimating aqueous solubility (log SW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, the correlation between the 3D-HoVAIF descriptors of PCDD/Fs and aqueous solubility (log SW) was established by partial least square (PLS) regression. The predictive power of the model was validated by leave-one-out cross-validated analysis. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

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