Based on the methodology of artificial neural networks, models describing the dependence of the level of RAGE inhibitory activity on the affinity of compounds for target proteins of the RAGE-NF-kB signal pathway have been costructed. A validated database of the structures and activity levels of 183 known compounds, which were tested for RAGE inhibitory activity was formed. The analysis of the AGE-RAGE signaling pathways was carried out, 14 key RAGE-NF-kB signal pathway nodes were found, for which 34 relevant target proteins were identified. A database of 66 valid 3D models of 22 target proteins of the RAGE-NF-kB signal chain was compiled. Ensemble molecular docking of 3D models of 183 known RAGE inhibitors into sites of 66 valid 3D models of 22 relevant RAGE target proteins was performed and minimum docking energies for each compound were determined for each target. According to the method of artificial multilayer perceptron neural networks, classification models were constructed to predict level of RAGE inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-kB signaling chain. The prognostic ability of these models of RAGE-inhibitory activity was evaluated, the maximum accuracy according to ROC-analysis was 90% for a high level of activity. The sensitivity analysis of the developed multitarget models were carried out, the most significant targets of the RAGE-NF-kB signal transmission chain were determined. It was found that for high level of RAGE inhibitory activity, the most significant biotargets are not AGE receptors, but eight signaling kinases of the RAGE-NF-kB pathway and transcription factor NF-kB1. Thus, it is suggested that known compounds with high RAGE-inhibitory activity are preferential inhibitors of signal kinases.
Vassiliev P.M. et al. Neural network modeling of multitarget RAGE inhibitory activity // Biomeditsinskaya khimiya. - 2019. - V. 65. -N 2. - P. 91-98.
Vassiliev P.M. et al., "Neural network modeling of multitarget RAGE inhibitory activity." Biomeditsinskaya khimiya 65.2 (2019): 91-98.
Vassiliev, P. M., Spasov, A. A., Yanaliyeva, L. R., Kochetkov, A. N., Vorfolomeyeva, V. V., Klochkov, V. G., Appazova, D. T. (2019). Neural network modeling of multitarget RAGE inhibitory activity. Biomeditsinskaya khimiya, 65(2), 91-98.