Abstract
Alzheimer's disease (AD) is a common neurodegenerative dementia in the elderly. Although there is no effective drug to treat AD, proteins associated with AD have been discovered in related studies. One of the proteins is mitochondrial fusion protein 2 (Mfn2), and its regulation presumably be related to AD. However, there is no specific drug for Mfn2 regulation. In this study, a three-tunnel deep neural network (3-Tunnel DNN) model is constructed and trained on the extended Davis dataset. In the prediction of drug-target binding affinity values, the accuracy of the model is up to 88.82% and the loss value is 0.172. By ranking the binding affinity values of 1,063 approved drugs and small molecular compounds in the DrugBank database, the top 15 drug molecules are recommended by the 3-Tunnel DNN model. After removing molecular weight <200 and topical drugs, a total of 11 drug molecules are selected for literature mining. The results show that six drugs have effect on AD, which are reported in references. Meanwhile, molecular docking experiments are implemented on the 11 drugs. The results show that all of the 11 drug molecules could dock with Mfn2 successfully, and 5 of them have great binding effect.
Highlights
Alzheimer’s disease (AD) is a destructive nervous system disease, which is characterized by a progressive dementia
Consistency index (CI) (Pahikkala et al, 2014) is used to evaluate the training performance, and mean square error (MSE) (Kansal et al, 2019) is used as the loss function to measure the error of each epoch
The original CNN_CNN model (Huang et al, 2020) and other models obtained in the DeepPurpose toolkit using our extended Davis dataset, such as CNN_CNN, convolutional neural network (CNN)+LSTM_CNN (CNN+long short-term memory units (LSTM) for drugs encoding, CNN for proteins encoding), and CNN+GRU_CNN (CNN+gated recurrent units (GRU) for drugs encoding, CNN for proteins encoding) model, are compared with the 3Tunnel DNN model
Summary
Alzheimer’s disease (AD) is a destructive nervous system disease, which is characterized by a progressive dementia. The DeepDTA model (Ozturk et al, 2018) considers the sequence information of drug molecules and proteins in the prediction of binding affinity values. The latest DeepGS model (Lin et al, 2020) inputs the sequence information and two-dimensional structure information of drug molecules as well as the protein sequence information into the model for prediction It has the problem of higher calculation cost. Two kinds of encoding methods are selected to input the model to predict the binding affinity values of DT pairs. We implement an approach that considers the binding affinity information and negative samples of DT pairs to reposition regulatory drugs Mfn as candidate medications of AD. After removing three molecules with molecular weight
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