Аннотация:
A new approach to the quantitative description of organic compound reactivity based on the neural network modelling of structure– reaction conditions–rate constant relationships was demonstrated for the acid hydrolysis of esters.
Тип публикации:
Статья
Язык публикации: английский
Образец цитирования:
N. M. Halberstam, I. I. Baskin, V. A. Palyulin, N. S. Zefirov, “Quantitative structure–conditions–property relationship studies. Neural network modelling of the acid hydrolysis of esters”, Mendeleev Commun., 12:5 (2002), 185–186
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/mendc4154
https://www.mathnet.ru/rus/mendc/v12/i5/p185
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T. I. Madzhidov, A. Rakhimbekova, V. A. Afonina, T. R. Gimadiev, R. N. Mukhametgaliev, R. I. Nugmanov, I. I. Baskin, A. Varnek, “Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow”, Mendeleev Commun., 31:6 (2021), 769–780
Natalia Kireeva, Vladislav S. Pervov, “Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches”, Phys. Chem. Chem. Phys., 19:31 (2017), 20904
T. I. Madzhidov, T. R. Gimadiev, D. A. Malakhova, R. I. Nugmanov, I. I. Baskin, I. S. Antipin, A. A. Varnek, “Structure–reactivity relationship in Diels–Alder reactions obtained using the condensed reaction graph approach”, J Struct Chem, 58:4 (2017), 650
John C. Dearden, Philip H. Rowe, Methods in Molecular Biology, 1260, Artificial Neural Networks, 2015, 65
G. Marcou, J. Aires de Sousa, D. A. R. S. Latino, A. de Luca, D. Horvath, V. Rietsch, A. Varnek, “Expert System for Predicting Reaction Conditions: The Michael Reaction Case”, J. Chem. Inf. Model., 55:2 (2015), 239
T. I. Madzhidov, P. G. Polishchuk, R. I. Nugmanov, A. V. Bodrov, A. I. Lin, I. I. Baskin, A. A. Varnek, I. S. Antipin, “Structure-reactivity relationships in terms of the condensed graphs of reactions”, Russ J Org Chem, 50:4 (2014), 459
R. I. Nugmanov, T. I. Madzhidov, G. R. Khaliullina, I. I. Baskin, I. S. Antipin, A. A. Varnek, “Development of “structure-property” models in nucleophilic substitution reactions involving azides”, J Struct Chem, 55:6 (2014), 1026
A. A. Kravtsov, P. V. Karpov, I. I. Baskin, V. A. Palyulin, N. S. Zefirov, “Prediction of rate constants of S N 2 reactions by the multicomponent QSPR method”, Dokl Chem, 440:2 (2011), 299
A. A. Kravtsov, P. V. Karpov, I. I. Baskin, V. A. Palyulin, N. S. Zefirov, “Prediction of the preferable mechanism of nucleophilic substitution at saturated carbon atom and prognosis of S N 1 rate constants by means of QSPR”, Dokl Chem, 441:1 (2011), 314
Jesús Jover, Ramón Bosque, Joaquim Sales, “QSPR Prediction of pKa for Benzoic Acids in Different Solvents”, QSAR Comb. Sci., 27:5 (2008), 563
N. I. Zhokhova, I. I. Baskin, V. A. Palyulin, A. N. Zefirov, N. S. Zefirov, “Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies”, Dokl Chem, 417:2 (2007), 282
Jesús Jover, Ramón Bosque, Joaquim Sales, “Neural Network Based QSPR Study for Predicting pKa of Phenols in Different Solvents”, QSAR Comb. Sci., 26:3 (2007), 385
Igor I. Baskin, Vladimir A. Palyulin, Nikolai S. Zefirov, Methods in Molecular Biology™, 458, Artificial Neural Networks, 2006, 133
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