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