The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.
【저자키워드】 SARS-CoV-2, deep learning, drug-target interaction, 【초록키워드】 COVID-19, Vaccine, Pneumonia, drugs, drug, virus, inhibitors, database, binding affinity, Novel coronavirus, Spread, Protein, Clinical studies, dataset, bidirectional, experiment, medication, inhibitor, information, predict, Amino acid, Interaction, drug-target, index, Amino acid sequences, FDA-approved drug, sequence, increased mortality, heterogeneous, protein sequences, approach, effective, Davis, highest, identify, performed, evaluated, approved, hidden, Bank, MSE, SMILES, 【제목키워드】 COVID-19, repurposing, drug, learning, deep,