Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

RIBONE, SERGIO R.; PAZ, S. ALEXIS; ABRAMS, CAMERON F.; VILLARREAL, MARCOS A.

J. COMPUT. AIDED MOL. RESIGN

Springer

Año: 2021

0920-654X

creening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing t