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Schlagwörter	Chemistry ; QD1-999
Sprache	Englisch
Erscheinungsdatum	2021-02-01T00:00:00Z
Verlag	American Chemical Society
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

Buch ; Online: 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

Kelvin Cooper / Christopher Baddeley / Bernie French / Katherine Gibson / James Golden / Thiam Lee / Sadrach Pierre / Brent Weiss / Jason Yang

2020

Abstract: 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 ... ...

Abstract	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.
Schlagwörter	Drug Discovery and Drug Delivery Systems ; SARS-CoV-2 ; COVID-19 ; Artificial Intelligence ; QSAR modeling approaches ; Random Forest ; Cluster Analysis ; Plasma Kallikrein ; HIV protease ; NSP5 ; NSP12 ; JAK 1 ; JAK 2 ; JAK 3 ; Angiotensin II receptors ; covid19
Erscheinungsdatum	2020-10-29T06:02:04Z
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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

Volltext online

Zusatzmaterialien

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Fernleihe an ZB MED

Buch ; Online: 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

Volltext online

Zusatzmaterialien

Kategorien

Fernleihe an ZB MED