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[関連PubMed/MEDLINE情報] 合計出現文献数: 15
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略語 |
: ASNN |
展開形 |
: Associative Neural Networks |
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No. |
発表年 |
題目 |
共起略語 |
1 |
2016 |
Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies. |
ILs, KNN, MIC, QSAR, RF |
2 |
2013 |
A big data approach to the ultra-fast prediction of DFT-calculated bond energies. |
3df,2p, BDE, DFT, ML |
3 |
2013 |
Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction. |
OLS, PLS |
4 |
2012 |
QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids. |
RF, SVM |
5 |
2011 |
A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. |
kNN, RT, SVM |
6 |
2011 |
The structure-antituberculosis activity relationships study in a series of 5-aryl-2-thio-1,3,4-oxadiazole derivatives. |
ETM-NN, SOMs |
7 |
2009 |
The structure-AChE inhibitory activity relationships study in a series of pyridazine analogues. |
ETM, FFNNs, SAR |
8 |
2008 |
Associative neural network. |
--- |
9 |
2008 |
Modeling the excitation wavelengths (lambda(ex)) of boronic acids. |
PLS, QSPR, SMLR, UFS |
10 |
2007 |
Prediction of 1H NMR coupling constants with associative neural networks trained for chemical shifts. |
FFNN |
11 |
2007 |
Synthesis and structure-antibacterial activity relationship investigation of isomeric 2,3,5-substituted perhydropyrrolo[3,4-d]isoxazole-4,6-diones. |
ETM, SOM |
12 |
2006 |
The structure--antituberculosis activity relationships study in a series of 5-(4-aminophenyl)-4-substituted-2,4-dihydro-3h-1,2,4-triazole-3-thione derivatives. A combined electronic-topological and neural networks approach. |
ETM-NN, SOM |
13 |
2004 |
The impact of available experimental data on the prediction of 1H NMR chemical shifts by neural networks. |
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14 |
2002 |
Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. |
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15 |
2002 |
Neural network studies. 4. Introduction to associative neural networks. |
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