The Consensus Ensemble Neural Network Multitarget Model of the RAGE Inhibitory Activity of Chemical Compounds

Abstract

RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe diabetic complications. RAGE inhibitors are promising pharmacological compounds; their development requires creation of new predictive models. Based on the methodology of artificial neural networks, a consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain has been created using the database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from the verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of learning by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were demonstrated. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.