A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Aerosol particulate matter (PM 10 and PM 2.5 ) and trace gases (SO 2 , NO 2 , CO and O 3 ) were sampled at five locations in greater Dhaka, Bangladesh, between January and April 2006. Particulate matter was collected on microfiber filters with a low-volume sampler, and trace gases (SO 2 , NO 2 , and O 3 ) were collected with an impinger equipped with PM samplers. Carbon monoxide was determined using the Indicator Tube method. The total average concentrations of SO 2 , NO 2 , CO, and O 3 were 48.3, 21.0, 166.0 and 28 μg m -3 , respectively. The total average concentrations of SO 2 and NO 2 were much lower than the annual average guideline values of the World Health Organization (WHO). The total average O 3 concentration was also much lower than the daily maximum values established by WHO (average of 100 μg m -3 for an 8-h sample). The total average concentrations of five sites were 263, 75.5 and 66.2 μg m -3 for SPM, PM 10 and PM 2.5 , respectively. The mass of PM 2.5 is approximately 88% of the PM 10 mass, indicating that fossil fuel is the main source of PM in Dhaka. An atomic absorption spectrophotometer was used to determine the heavy metal concentrations in the PM 2.5 size fraction. The total average concentrations of As, Cd, Cu, Fe, Pb, and Zn in PM 2.5 were 6.3, 13, 94, 433, 204, and 381 ng m -3 , respectively. The Pb concentration in Dhaka shows a decreasing tendency, presumably due to the ban on the use of leaded fuel. The overall trace metal concentrations in Dhaka are higher than those in European (e.g., Spain, Norway) and East Asian (e.g., Taiwan) locations, but lower than those measured in Southeast Asian (Kanpur, Delhi, Mumbai, India; Lahore, Pakistan) cities.