Affiliation:
1. FSBEI HE VolSMU of the Ministry of Health of Russia
Abstract
As a result of work to create the optimal quantum-chemical neural network model for antiglycation activity prediction, a set of different neural network models was received.
Funder
Council on grants of the President of the Russian Federation
Publisher
CO LTD "EXPO-BIOHIM-TEXNOLOGIES"
Reference5 articles.
1. 1. Frau J., Glossman-Mitnik D. Chemical Reactivity Theory Study of Advanced Glycation Endproduct Inhibitors // Molecules. 2017. Vol. 22(2). 226.
2. 2. Litvinov, R. A., Drokin, R. A., Shamshina, D. D. et al. Prediction of Antiglycation Activity by Calculating the Energies of Frontier Molecular Orbitals for New 4-Hydroxy-1,4-Dihydroazolo[5,1-c]-1,2,4-Triazines Used as an Example // Russ J Bioorg Chem. 2020. Vol. 46, P. 1278–1284.
3. 3. Litvinov R. A., Vasil’ev P. M., Brel’ A. K., Lisina S. V. Boundary molecular orbital energies as descriptors for prediction of antiglycating activity of N-hydroxybenzoyl-substituted thymine and uracil // Khimiko-Farmatsevticheskii Zhurnal. 2021. Vol. 55. No 7. P. 18-24. [Article in Russian, Abstract in English].
4. 4. Litvinov R. A., Lisina S. V., Brel A. K. et al. Nejrosetevaya model' antiglikiruyushchej aktivnosti N-gidroksibenzoil proizvodnyh timina i uracila [Neural network model of antiglycation activity prediction of N-hydroxybenzoyl derivatives of thymine and uracil] // VI Interdisciplinary Conference «Molecular and Biological Aspects of Chemistry, Pharmaceuticals and Pharmacology»: abstr. (Nizhny Novgorod, Sept. 27-30 2020). – Nizhny Novgorod, 2020. – P. 62. [Abstract in Russian].
5. 5. Litvinov R. A., Vasilev P. M., Drokin R. A. et al. Kvantovo-himicheskaya nejrosetevaya QSAR-model' prognoza antiglikiruyushchej aktivnosti proizvodnyh azolotriazina [Quantum-chemical neural network QSAR-model for prediction the antiglycation activity of azolotriazine derivatives] // XXVII Symposium on bioinformatics and computer-aided drug discovery: abstr. (Moscow, Apr. 5-7 2021). – Moscow, 2021.