Mechanistic interpretation of artificial neural network-based QSAR model for prediction of cathepsin K inhibition potency

2014 ◽  
Vol 28 (4) ◽  
pp. 272-281 ◽  
Author(s):  
Jure Borišek ◽  
Viktor Drgan ◽  
Nikola Minovski ◽  
Marjana Novič
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cathepsin K ◽  
Qsar Model ◽  
Artificial Neural ◽  
Inhibition Potency ◽  
Cathepsin K Inhibition

scholarly journals Kajian HKSA Antimalaria Senyawa Turunan Quinolon-4(1H)-imines Menggunakan Metode MLR-ANN

2019 ◽  
Vol 1 (1) ◽  
pp. 21-26
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Qsar Model ◽  
Ann Model ◽  
Multilinear Regression ◽  
Qsar Study ◽  
Structure Activity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Quantitatif Structure-Activity Relationship (QSAR) study of 22 antimalarial compounds of Quinolon-4(1H)-imines derivatives has been done using multilinear regression (MLR) and artificial neural network (ANN) methods. The best QSAR model was obtained from ANN analysis indicated by its higher correlation coefficient (r2) compared to MLR method, i.e. 0.931 with most influential descriptors is qC1, qC5, qC11, qN14 and log P.Keywords: Quinolon-4(1H)-imines, Antimalarial, QSAR, MLR-ANNTelah dilakukan kajian analisis Hubungan Kuantitatif Struktur Aktivitas (HKSA) terhadap 22 senyawa antimalaria turunan Quinolon-4(1H)-imines menggunakan metode regresi multilinear (MLR) dan artificial neural network (ANN). Model HKSA terbaik diperoleh dari hasil analisis menggunakan metode ANN yang ditunjukkan oleh nilai koefisien korelasi (r2) paling tinggi dibandingkan dengan metode MLR yaitu sebesar 0,931 dengan deskriptor paling berpengaruh terhadap aktivitas antimalaria turunan Quinolon-4(1H)-imines, yaitu qC1, qC5, qC11, qN14 dan log P.Kata Kunci: Quinolon-4(1H)-imines, Antimalaria, HKSA, MLR-ANN

scholarly journals PREDICTING ESTROGEN ACTIVITIES OF BISPHENOL A AND ITS ANALOGS USING QUANTUM CHEMISTRY CALCULATIONS AND ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Vol 62 (5) ◽  
pp. 31-37
Author(s):  
Vu Van Dat ◽  
Le Kim Long ◽  
Nguyen Hoang Trang ◽  
Doan Van Phuc ◽  
Nguyen Van Trang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Quantum Chemistry ◽  
Qsar Model ◽  
Quantum Chemistry Calculations ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

This article presents the results of the quantitative structure – activity relationship (QSAR) study of bisphenol A (BPA) and its analogs using quantum chemistry calculations and method of artificial neural networks (ANN). Molecular structural analysis is performed using Density Functional Theory (DFT) at the B3LYP/6-31+G(d) level. The quantum calculations focus on finding the optimized molecular structures, vibrational frequencies, the molecular orbital energies with reasonable accuracy. The study of electron density distribution was carried out in the framework of the natural bond orbital (NBO) methods. The obtained parameters and known observable estrogen activities are used as input data for constructing the QSAR model, using the artificial neural network method. Based on the artificial neural network method the quantum parameters having the strongest impact on the estrogen activity of the compounds were revealed. The internal and external validation methods have been performed to test the performance and the stability of the model. The statistical parameters obtained of the QSAR model were: R2 = 0.99; Q2LOO = 0.98; R2Predict = 0.98. According to the obtained results, our proposed model, constructing by method of artificial neural network using the parameters of quantum chemistry is adequate and may be useful to predict of estrogen activities for unexplored derivatives and BPA analogs with moderate reliability.

scholarly journals Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage

2021 ◽  
Vol 22 (20) ◽  
pp. 10995
Author(s):  
Taeho Kim ◽  
Byoung Hoon You ◽  
Songhee Han ◽  
Ho Chul Shin ◽  
Kee-Choo Chung ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Blood Brain Barrier ◽  
3D Qsar ◽  
Qsar Model ◽  
Brain Barrier ◽  
Quantum Mechanical ◽  
Blood Brain ◽  
Artificial Neural ◽  
Bbb Permeability

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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