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 vol. 36 p. 25 - 25

0920-654X

creening already approved drugs for activity against a novel pathogen can be an important part of global rapid-responsestrategies in pandemics. Such high-throughput repurposing screens have already identifed several existing drugs with poten-tial to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifcally againstthis pathogen requires unambiguous identifcation of their corresponding targets, something the high-throughput screens arenot typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom proteinstructures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plural-ity of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method withknown drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentiallysuitable for repurposing to the i