scDock

scDock integrates scRNA-seq analysis, cell-cell communication inference, and molecular docking to identify drug candidates targeting intercellular communication networks.

Key Features:

• Single-cell RNA sequencing data processing: Processes raw scRNA-seq data through a complete workflow.
• Cell-cell communication inference: Automates identification of disease-relevant ligand-receptor interactions from scRNA-seq data.
• Structure-based virtual screening: Performs molecular docking against communication targets using Protein Data Bank or AlphaFold-predicted protein structures.
• Integrated pipeline execution: Connects scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery through a single configuration file.
• Ranked drug candidate output: Generates ranked drug candidates from raw scRNA-seq data.
• Comprehensive stage outputs: Generates outputs at each stage to explore intercellular signaling alterations.

Scientific Applications:

• Drug discovery targeting intercellular communication networks: Identifies therapeutic compounds targeting specific cell-cell communications in disease contexts.
• Disease-relevant ligand-receptor interaction analysis: Identifies altered intercellular signaling from single-cell sequencing data.
• Structure-based drug screening: Screens drug candidates against protein structures from Protein Data Bank or AlphaFold predictions.

Methodology:

scDock is implemented in R, Python, and shell scripts. The pipeline processes scRNA-seq data, infers cell-cell communication to identify ligand-receptor interactions, and performs molecular docking against these targets using Protein Data Bank or AlphaFold-predicted protein structures.