Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.
Tesia BobrowskiVinicius M AlvesCleber C Melo-FilhoDaniel KornScott S AuerbachCharles SchmittEugene MuratovAlexander TropshaPublished in: ChemRxiv : the preprint server for chemistry (2020)
The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation.<br>.
Keyphrases
- molecular docking
- sars cov
- respiratory syndrome coronavirus
- molecular dynamics simulations
- global health
- clinical trial
- public health
- endothelial cells
- electronic health record
- emergency department
- healthcare
- big data
- risk assessment
- machine learning
- human health
- drug administration
- climate change
- coronavirus disease
- induced pluripotent stem cells
- phase ii
- deep learning
- study protocol
- case control