Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity
This work addresses the urgent need for drug repurposing to combat SARS-CoV-2, offering a computational approach to accelerate drug development, though it appears incremental as it builds on existing deep learning techniques.
The authors tackled the problem of predicting drug-target binding affinity for SARS-CoV-2 by proposing DeepGLSTM, a graph convolutional network and LSTM-based method, which predicted a combined score for 2,304 FDA-approved drugs against 5 viral proteins and identified a top-18 list of drugs with the highest binding affinity.
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.