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2. DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

Author

Abdel-Basset, Mohamed, Hawash, Hossam, Elhoseny, Mohamed, Chakrabortty, Ripon K., and Ryan, Michael

Subjects
Drug, General Computer Science, Coronavirus disease 2019 (COVID-19), Computer science, media_common.quotation_subject, Drug target, Computers and Information Processing, Computational biology, Molecule, drug-target interaction, General Materials Science, media_common, chemistry.chemical_classification, SARS-CoV-2, business.industry, Deep learning, General Engineering, TK1-9971, Amino acid, Identification (information), Drug repositioning, chemistry, Graph (abstract data type), Computational and Artificial Intelligence, Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business
Abstract

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.

Published
2020

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