Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures

Abstract

The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of − 7.0 kcal/mol and − 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization

“…Three VDA datasets are obtained from Peng et al (2021). Each dataset contains virus similarity matrix, drug similarity matrix, and VDA matrix.…”

“…Zhou L. et al (2020) exploited a KATZ algorithm (VDA-KATZ) to predict candidate drugs for the SARS-CoV-2 prevention on the VDA dataset. Peng et al (2021) continued to construct two VDA datasets and developed a random walk with restart method (VDA-RWR) to prioritize drugs related to COVID-19. Zhou Y. et al (2020) designed a formidable network-based method to reposition the existing chemical agents and quickly screened latent drug combinations for COVID-19.…”

“…In this work, we propose a Virus-Drug Association (VDA) prediction algorithm, VDA-GBNNR, to discover potential chemical agents against COVID-19 based on virus similarity, drug similarity, VDA network, Gaussian Association Profile Kernel (GAPK), and Bounded Nuclear Norm Regularization (BNNR). VDA-GBNNR is compared with three existing VDA prediction methods, that is, VDA-RLSBN (Peng et al, 2020), VDA-KATZ (Zhou L. et al, 2020), VDA-RWR (Peng et al, 2021) and one network-based microRNAanticancer drug association prediction model SMiR-NBI (Li et al, 2016) on three VDA datasets. The experimental results show that VDA-GBNNR computes the best AUC, accuracy, sensitivity, and specificity.…”

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

“…The drugs against the Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV), such as Lopinavir/ritonavir, have been proved to inhibit many viruses (22,23). As a nucleoside drug and RNA polymerase (RdRp) inhibitor, remandsivir can inhibit SARS-CoV-2 RdRp, subgenomic mRNA and subviral genomic RNA to block the synthesis of negative chain RNA, thus inhibiting virus replication and antiviral effect (24)(25)(26).…”

Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19

“…Patients with COVID-19 but with no symptoms may have transmitted the virus to close contacts before a definite diagnosis could be made. Though there are many studies on identifying effective drugs against SARS-CoV-2 ( Tang et al, 2020 ; Peng et al, 2021 ), most of them need further experimental and clinical validation ( Peng et al, 2020 ; Zhou et al, 2020 ). Therefore, early diagnosis was extremely important.…”