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

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.

Interactions Between Remdesivir, Ribavirin, Favipiravir, Galidesivir, Hydroxychloroquine and Chloroquine with Fragment Molecular of the COVID-19 Main Protease with Inhibitor N3 Complex (PDB ID:6LU7) Using Molecular Docking.

We started a study on the molecular docking of six potential pharmacologically active inhibitors compounds that can be used clinically against the COVID-19 virus, in this case, remdesivir, ribavirin, favipiravir, galidesivir, hydroxychloroquine and chloroquine interacting with the main COVID-19 protease in complex with a COVID-19 N3 protease inhibitor. The highest values of affinity energy found in order from highest to lowest were chloroquine (CHL), hydroxychloroquine (HYC), favipiravir (FAV), galidesivir (GAL), remdesivir (REM) and ribavirin (RIB). The possible formation of hydrogen bonds, associations through London forces and permanent electric dipole were analyzed. The values of affinity energy obtained for the hydroxychloroquine ligands was -9.9 kcal/mol and for the chloroquine of -10.8 kcal/mol which indicate that the coupling contributes to an effective improvement of the affinity energies with the protease. Indicating that, the position chosen to make the substitutions may be a pharmacophoric group, and cause changes in the protease.

Investigation of Some Antiviral N-Heterocycles as COVID 19 Drug: Molecular Docking and DFT Calculations.

The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease Mpro, because of its essential role in processing the polyproteins that are translated from the viral RNA. In this study, the bioactivity of some selected heterocyclic drugs named Favipiravir (1), Amodiaquine (2), 2'-Fluoro-2'-deoxycytidine (3), and Ribavirin (4) was evaluated as inhibitors and nucleotide analogues for COVID-19 using computational modeling strategies. The density functional theory (DFT) calculations were performed to estimate the thermal parameters, dipole moment, polarizability, and molecular electrostatic potential of the present drugs; additionally, Mulliken atomic charges of the drugs as well as the chemical reactivity descriptors were investigated. The nominated drugs were docked on SARS-CoV-2 main protease (PDB: 6LU7) to evaluate the binding affinity of these drugs. Besides, the computations data of DFT the docking simulation studies was predicted that the Amodiaquine (2) has the least binding energy (-7.77 Kcal/mol) and might serve as a good inhibitor to SARS-CoV-2 comparable with the approved medicines, hydroxychloroquine, and remdesivir which have binding affinity -6.06 and -4.96 Kcal/mol, respectively. The high binding affinity of 2 was attributed to the presence of three hydrogen bonds along with different hydrophobic interactions between the drug and the critical amino acids residues of the receptor. Finally, the estimated molecular electrostatic potential results by DFT were used to illustrate the molecular docking findings. The DFT calculations showed that drug 2 has the highest of lying HOMO, electrophilicity index, basicity, and dipole moment. All these parameters could share with different extent to significantly affect the binding affinity of these drugs with the active protein sites.

Anti-HCV, nucleotide inhibitors, repurposing against COVID-19.

AIMS: A newly emerged Human Coronavirus (HCoV) is reported two months ago in Wuhan, China (COVID-19). Until today >2700 deaths from the 80,000 confirmed cases reported mainly in China and 40 other countries. Human to human transmission is confirmed for COVID-19 by China a month ago. Based on the World Health Organization (WHO) reports, SARS HCoV is responsible for >8000 cases with confirmed 774 deaths. Additionally, MERS HCoV is responsible for 858 deaths out of about 2500 reported cases. The current study aims to test anti-HCV drugs against COVID-19 RNA dependent RNA polymerase (RdRp). MATERIALS AND METHODS: In this study, sequence analysis, modeling, and docking are used to build a model for Wuhan COVID-19 RdRp. Additionally, the newly emerged Wuhan HCoV RdRp model is targeted by anti-polymerase drugs, including the approved drugs Sofosbuvir and Ribavirin. KEY FINDINGS: The results suggest the effectiveness of Sofosbuvir, IDX-184, Ribavirin, and Remidisvir as potent drugs against the newly emerged HCoV disease. SIGNIFICANCE: The present study presents a perfect model for COVID-19 RdRp enabling its testing in silico against anti-polymerase drugs. Besides, the study presents some drugs that previously proved its efficiency against the newly emerged viral infection.