By injection into a vein, it is used for colon cancer, esophageal cancer, stomach cancer, pancreatic cancer, breast cancer, and cervical cancer
By injection into a vein, it is used for colon cancer, esophageal cancer, stomach cancer, pancreatic cancer, breast cancer, and cervical cancer. offers a foundation for further experimental studies of COVID-19 drug repositioning. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China, in late December 2019 and has rapidly spread around the world. By June 11, 2020, over 7.1 million individuals were infected, and more than 408?000 fatalities had been reported. Currently, there is no specific antiviral drug for this epidemic. It is worth noting that recently, an experimental drug, Remdesivir, has been recognized as a promising anti-SARS-CoV-2 drug. However, the high experimental value of IC50 (11.41 M)1 indicates that it must be used in a large dose in treating COVID-19, which is subject to side effects. Considering the severity of this widespread dissemination and health threats, panicked patients misled by media flocked to pharmacies 20-HEDE for Chinese medicine herbs, which were reported to inhibit SARS-CoV-2, despite no clinical evidence supporting the claim. Although there is also no evidence for Chloroquines claimed curing effect, some desperate people take it as prophylactic for COVID-19. Many researchers are engaged in developing anti-SARS-CoV-2 drugs.2,3 However, new drug discovery is a long, costly, and rigorous scientific process. A more effective approach is to search for anti-SARS-CoV-2 therapies from existing drug databases. Drug repositioning (also known as drug repurposing), which concerns the investigation of existing drugs for new therapeutic target indications, has emerged as a successful strategy for drug discovery because of the reduced costs and expedited approval procedures.4?6 Several successful examples reveal its great value in 20-HEDE practice: Nelfinavir, initially developed to treat the human immunodeficiency virus (HIV), is now being used for cancer treatments. Amantadine was first designed to treat the influenza caused by type A influenza viral infection and is being used for the Parkinsons disease.7 In recent years, the rapid growth of drug-related data sets, as well as open data initiatives, has led to new developments for computational drug repositioning, particularly structural-based drug repositioning (SBDR). Machine learning, network analysis, and text mining and semantic inference are three major computational approaches commonly applied in drug repositioning.8 The rapid accumulation of genetic and structural databases (https://www.rcsb.org/ and https://www.ncbi.nlm.nih.gov/genbank/), the development of low-dimensional mathematical representations of complex biomolecular structures,9 and the availability of advanced deep learning algorithms have made machine learning-based drug repositioning a promising approach.8 Because of the urgent need for anti-SARS-CoV-2 drugs, a computational Bmp2 drug repositioning is one of the most feasible strategies for discovering SARS-CoV-2 drugs. In SBDR, one needs to select one or a few effective targets. Study shows that the SARS-CoV-2 genome is very close to that of the severe acute respiratory syndrome (SARS)-CoV.10 The sequence identities of SARS-CoV-2 3CL protease, RNA polymerase, and the spike protein with corresponding SARS-CoV proteins are 96.08%, 96%, and 76%, respectively11 (see Figure S1). We, therefore, hypothesize that a potent SARS 3CL protease inhibitor is also a potent SARS-CoV-2 3CL protease inhibitor. Unfortunately, there is no effective SARS therapy at present. Nevertheless, the X-ray crystal structures of both SARS and SARS-CoV-2 3CL proteases have been reported.12,13 Additionally, the binding affinities of SARS-CoV or SARS-CoV-2 3CL protease inhibitors from single-protein experiments are available in various databases or the original literature. Moreover, the DrugBank contains about 1600 drugs approved by the U.S. Food and Drug Administration (FDA) as well as more than 7000 investigational or off-market drugs.14 The aforementioned information provides a sound basis for developing an SBDR machine learning model for SARS-CoV-2 3CL protease inhibition. It 20-HEDE is worth clarifying that SBDR machine learning models are driven by data and do not explicitly form the energy terms related to some biophysical characteristics such as electrostatics and hydrogen bonding. Instead, these biophysical interactions are implicitly encoded in.