Artificial neural networks: Non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors

2004 ◽  
Vol 8 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Latifa Douali ◽  
Didier Villemin ◽  
Abdelmajid Zyad ◽  
Driss Cherqaoui
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reverse Transcriptase ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Non Linear ◽  
Artificial Neural ◽  
Hept Derivatives ◽  
Hiv 1

scholarly journals Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by artificial neural networks: HEPT derivatives

ARKIVOC ◽  
2007 ◽  
Vol 2007 (14) ◽  
pp. 245-256 ◽  
Author(s):  
Mohamed Zahouily ◽  
Jamila Rakik ◽  
Mohamed Lazar ◽  
Moulay A. Bahlaoui ◽  
Ahmed Rayadh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reverse Transcriptase ◽  
Reverse Transcriptase Inhibitors ◽  
Nucleoside Reverse Transcriptase Inhibitors ◽  
Artificial Neural ◽  
Hept Derivatives

HIV-1 Reverse Transcriptase Inhibitor Design Using Artificial Neural Networks

1994 ◽  
Vol 37 (16) ◽  
pp. 2520-2526 ◽  
Author(s):  
Igor V. Tetko ◽  
Vsevolod Yu. Tanchuk ◽  
Neliya P. Chentsova ◽  
Svetlana V. Antonenko ◽  
Gennady I. Poda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reverse Transcriptase ◽  
Transcriptase Inhibitor ◽  
Inhibitor Design ◽  
Artificial Neural ◽  
Reverse Transcriptase Inhibitor ◽  
Hiv 1

3D-QSAR Studies of S-DABO Derivatives as Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

2019 ◽  
Vol 16 (8) ◽  
pp. 868-881
Author(s):  
Yueping Wang ◽  
Jie Chang ◽  
Jiangyuan Wang ◽  
Peng Zhong ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Binding Mode ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Hiv 1

Background: S-dihydro-alkyloxy-benzyl-oxopyrimidines (S-DABOs) as non-nucleoside reverse transcriptase inhibitors have received considerable attention during the last decade due to their high potency against HIV-1. Methods: In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) of a series of 38 S-DABO analogues developed in our lab was studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The Docking/MMFF94s computational protocol based on the co-crystallized complex (PDB ID: 1RT2) was used to determine the most probable binding mode and to obtain reliable conformations for molecular alignment. Statistically significant CoMFA (q2=0.766 and r2=0.949) and CoMSIA (q2=0.827 and r2=0.974) models were generated using the training set of 30 compounds on the basis of hybrid docking-based and ligand-based alignment. Results: The predictive ability of CoMFA and CoMSIA models was further validated using a test set of eight compounds with predictive r2 pred values of 0.843 and 0.723, respectively. Conclusion: The information obtained from the 3D contour maps can be used in designing new SDABO derivatives with improved HIV-1 inhibitory activity.

scholarly journals Analysis of Cantilever Beam Deflection under Uniformly Distributed Load using Artificial Neural Networks

2019 ◽  
Vol 255 ◽  
pp. 06004
Author(s):  
T.M.Y.S Tuan Ya ◽  
Reza Alebrahim ◽  
Nadziim Fitri ◽  
Mahdi Alebrahim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cantilever Beam ◽  
Training Data ◽  
Beam Deflection ◽  
Problem Solver ◽  
Distributed Load ◽  
Uniformly Distributed Load ◽  
Non Linear ◽  
Artificial Neural

In this study the deflection of a cantilever beam was simulated under the action of uniformly distributed load. The large deflection of the cantilever beam causes the non-linear behavior of beam. The prupose of this study is to predict the deflection of a cantilever beam using Artificial Neural Networks (ANN). The simulation of the deflection was carried out in MATLAB by using 2-D Finite Element Method (FEM) to collect the training data for the ANN. The predicted data was then verified again through a non linear 2-D geometry problem solver, FEM. Loads in different magnitudes were applied and the non-linear behaviour of the beam was then recorded. It was observed that, there is a close agreement between the predicted data from ANN and the results simulated in the FEM.

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