DockCoV2

DockCoV2 predicts binding affinities between a curated collection of 3,109 FDA-approved and Taiwan National Health Insurance (NHI)-covered drugs and seven SARS-CoV-2-related proteins to support identification of compounds that may inhibit viral entry or replication.

Key Features:

• Drug library: A curated collection of 3,109 FDA-approved medications and Taiwan NHI-covered drugs.
• Target proteins: Predictions cover seven proteins: spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), nucleocapsid (N) protein, human angiotensin-converting enzyme 2 (ACE2), and transmembrane serine protease TMPRSS2.
• Binding affinity predictions: Computational assessment of drug–protein binding affinities for each drug against the seven specified proteins.
• Experimental data integration: Inclusion of experimental efficacy data indicating which drugs have shown activity against related coronaviruses such as MERS and SARS-CoV.
• External database integration: Linkage to supplementary information from external databases for each drug and target.

Scientific Applications:

• Drug repurposing: Prioritize FDA-approved and Taiwan NHI drugs for evaluation against SARS-CoV-2.
• Inhibition of viral entry: Identify compounds that may disrupt spike–ACE2 interaction or TMPRSS2-mediated spike priming.
• Inhibition of viral replication: Identify compounds targeting replication-relevant proteins such as 3CLpro, RdRp, PLpro, or the N protein.
• Candidate prioritization: Narrow down compounds for experimental validation based on predicted binding affinities.
• Cross-reactivity insights: Use experimental data from MERS and SARS-CoV to inform potential cross-reactivity and repurposing opportunities.

Methodology:

State-of-the-art computational predictions were used to assess binding affinities between each of the 3,109 drugs and the seven specified proteins.