GuiltyTargets-COVID-19

GuiltyTargets-COVID-19 identifies and prioritizes candidate drug targets for COVID-19 by integrating bulk and single-cell RNA sequencing data, a lung tissue-specific protein-protein interaction network, ChEMBL ligand mappings, and machine learning.

Key Features:

• Target Identification and Prioritization: Uses bulk and single-cell RNA sequencing datasets to identify and prioritize potential COVID-19 drug targets based on biological relevance and druggability.
• Machine Learning Support: Applies machine learning to support the identification and ranking of candidate targets.
• Integration with Protein-Protein Interaction Networks: Incorporates a lung tissue-specific protein-protein interaction network to contextualize targets within COVID-19 pathophysiology.
• Ligand Mapping and Drug Repurposing: Maps identified targets to ligands in the ChEMBL database to link existing compounds, including approved and in-development drugs, to candidate targets.
• Side Effect Prediction: Assesses potential side effects of mapped ligands, particularly when they correspond to approved drugs.

Scientific Applications:

• Drug target discovery for COVID-19: Systematic identification and prioritization of candidate therapeutic targets using multi-omics and network data.
• Drug repurposing: Identification of existing ChEMBL compounds that map to prioritized targets for potential repurposing.
• Safety profiling: Evaluation of potential side effects for mapped ligands to inform feasibility of repurposing.
• Cell type- and tissue-specific analysis: Cell type- and lung tissue-specific target analysis enabled by bulk and single-cell RNA-seq combined with a lung-specific PPI network.
• Example discoveries: Identification of AKT3 from both bulk and single-cell RNA-seq data and of AKT2, MLKL, and MAPK11 in single-cell experiments.

Methodology:

Analyzes bulk and single-cell RNA sequencing datasets together with a lung tissue-specific protein-protein interaction network using machine learning to identify and prioritize targets, maps prioritized targets to ligands in ChEMBL, and assesses potential side effects of mapped ligands.