A unique approach
to bioactivity and chemical data curation coupled
with random forest analyses has led to a series of target-specific
and cross-validated predictive feature fingerprints (PFF) that have
high predictability across multiple therapeutic targets and disease
stages involved in the severe acute respiratory syndrome due to coronavirus
2 (SARS-CoV-2)-induced COVID-19 pandemic, which include plasma kallikrein,
human immunodeficiency virus (HIV)-protease, nonstructural protein
(NSP)5, NSP12, Janus kinase (JAK) family, and AT-1. The approach was
highly accurate in determining the matched target for the different
compound sets and suggests that the models could be used for virtual
screening of target-specific compound libraries. The curation-modeling
process was successfully applied to a SARS-CoV-2 phenotypic screen
and could be used for predictive bioactivity estimation and prioritization
for clinical trial selection; virtual screening of drug libraries
for the repurposing of drug molecules; and analysis and direction
of proprietary data sets.
<p>A unique approach to bioactivity and chemical data curation coupled with Random forest analyses has led to a series of target-specific and cross-validated Predictive Feature Fingerprints (PFF) that have high predictability across multiple therapeutic targets and disease stages involved in the SARS-CoV-2 induced COVID-19 pandemic, which include plasma kallikrein, HIV protease, NSP5, NSP12, JAK family and AT-1. The approach was highly accurate in determining the matched target for the different compound sets and suggests that the models could be used for virtual screening of target specific compound libraries. The curation-modeling process was successfully applied to a SARS-CoV-2 phenotypic screen and could be used for predictive bioactivity estimation and prioritization for clinical trial selection, virtual screening of drug libraries for repurposing of drug molecules, and analysis and direction of proprietary datasets.</p>
<p>A unique approach to bioactivity and chemical data curation coupled with Random forest analyses has led to a series of target-specific and cross-validated Predictive Feature Fingerprints (PFF) that have high predictability across multiple therapeutic targets and disease stages involved in the SARS-CoV-2 induced COVID-19 pandemic, which include plasma kallikrein, HIV protease, NSP5, NSP12, JAK family and AT-1. The approach was highly accurate in determining the matched target for the different compound sets and suggests that the models could be used for virtual screening of target specific compound libraries. The curation-modeling process was successfully applied to a SARS-CoV-2 phenotypic screen and could be used for predictive bioactivity estimation and prioritization for clinical trial selection, virtual screening of drug libraries for repurposing of drug molecules, and analysis and direction of proprietary datasets.</p>
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