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Development of new drugs is an expensive and time-consuming process. Due tothe world-wide SARS-CoV-2 outbreak, it is essential that new drugs forSARS-CoV-2 are developed as soon as possible. Drug repurposing techniques canreduce the time span needed to develop new drugs by probing the list ofexisting FDA-approved drugs and their properties to reuse them for combatingthe new disease. We propose a novel architecture DeepGLSTM, which is a GraphConvolutional network and LSTM based method that predicts binding affinityvalues between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Ourproposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity),DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novelarchitecture to predict a Combined Score (calculated using Davis and KIBAscore) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis ofthe Combined Score, we prepare a list of the top-18 drugs with the highestbinding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, thislist may be used for the creation of new useful drugs.

Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity