Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

Interdiscip Sci. 2020 Sep;12(3):368-376. doi: 10.1007/s12539-020-00376-6. Epub 2020 Jun 1.


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.

Keywords: 3C-like protease; Coronavirus; Deep learning; Drug screening; Homology modeling.

MeSH terms

  • Amino Acid Sequence
  • Antiviral Agents / chemistry
  • Antiviral Agents / pharmacology*
  • Betacoronavirus / drug effects*
  • Betacoronavirus / genetics
  • COVID-19
  • Catalytic Domain
  • Coronavirus 3C Proteases
  • Coronavirus Infections / drug therapy*
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / virology*
  • Cysteine Endopeptidases / chemistry
  • Cysteine Endopeptidases / genetics
  • Databases, Nucleic Acid
  • Databases, Pharmaceutical
  • Deep Learning*
  • Drug Design
  • Drug Evaluation, Preclinical / methods*
  • Drug Evaluation, Preclinical / statistics & numerical data
  • Humans
  • Ligands
  • Models, Molecular
  • Molecular Dynamics Simulation
  • Oligopeptides / chemistry
  • Oligopeptides / pharmacology
  • Pandemics
  • Pneumonia, Viral / drug therapy*
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / virology*
  • SARS-CoV-2
  • Sequence Alignment
  • Structural Homology, Protein
  • User-Computer Interface
  • Viral Nonstructural Proteins / antagonists & inhibitors*
  • Viral Nonstructural Proteins / chemistry
  • Viral Nonstructural Proteins / genetics


  • Antiviral Agents
  • Ligands
  • Oligopeptides
  • Viral Nonstructural Proteins
  • Cysteine Endopeptidases
  • Coronavirus 3C Proteases