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Predictive Feature Fingerprints.pdf (2.85 MB)

Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning

submitted on 27.10.2020, 12:54 and posted on 29.10.2020, 06:02 by Kelvin Cooper, Christopher Baddeley, Bernie French, Katherine Gibson, James Golden, Thiam Lee, Sadrach Pierre, Brent Weiss, Jason Yang

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.


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KC Pharma Consulting



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Declaration of Conflict of Interest

No conflict of interest