FDA-approved Pralatrexate identified by virtual drug screening inhibits SARS-CoV-2 replication in vitro (preprint)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses serious threats to the global public health and leads to an unprecedented worldwide crisis. Unfortunately, no effective drugs or vaccines are available till now. Since the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a promising therapeutic target, a deep learning and molecular simulation based hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected FDA-approved 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 accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov (preprint)

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. 2019-nCoV spreads more rapidly than SARS-CoV. Unfortunately, there is no drug to combat the virus. It is of high significance to develop a drug that can combat the virus effectively before the situation gets worse. 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 to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In this paper, we first collected virus RNA sequences from the GISAID database, translated the RNA sequences into protein sequences, and built a protein 3D model using homology modeling. Coronavirus main protease is considered to be a major therapeutic target, thus this paper focused on drug screening based on the modeled 2019-nCov_main_protease structure. The deep learning based method DFCNN, developed by our group, can identify/rank the protein-ligand interactions with relatively high accuracy. DFCNN is capable of performing virtual screening quickly since no docking or molecular dynamic simulation is needed. DFCNN identifies potential drugs for 2019-nCoV protease by performing drug screening against 4 chemical compound databases. Also, we performed drug screening for all tripeptides against the binding site of 2019-nCov_main_protease since peptides often show better stability, more bio-availability and negligible immune responses. In the end, we provided the list of possible chemical ligands and peptide drugs for experimental validation.