canSAR

canSAR integrates diverse publicly available biological annotations, chemical screening results, RNA interference screenings, gene expression data, amplification information, and 3D structural data to support cancer research and drug discovery.

Key Features:

• Data integration: Consolidates biological annotations, chemical screening results, RNA interference screenings, gene expression data, amplification information, and 3D structural data into a unified resource.
• Target synopses: Provides detailed summaries of targets including annotations, structural characterization, and expression information.
• Compound synopses: Provides detailed summaries of compounds including associated bioactivity and annotations.
• Bioactivity views: Presents bioactivity data linked to targets and compounds for analysis of ligand-target relationships.
• Chemogenomic analysis: Supplies tools for chemogenomic interrogation of target–compound relationships and similarities.
• Expression data analysis: Includes gene expression data and expression-level characterization for targets across datasets.
• RNA interference screening data: Integrates RNA interference screening results for functional interrogation of targets.
• Amplification information: Incorporates genomic amplification information relevant to cancer targets.
• 3D structural data: Contains three-dimensional structural data and structural annotations for targets.
• Protein interaction insights: Provides protein interaction data and network-level information for targets.
• Suitable cell line identification: Reports cell line annotations and suitability information for experimental selection.
• Potential tool compounds: Identifies candidate tool compounds associated with targets.
• Target similarity assessment: Reports similarities to known drug targets to inform target prioritization.

Scientific Applications:

• Cancer target identification and prioritization: Supports identification and ranking of cancer-relevant targets using integrated genetic, expression, structural, and screening data.
• Drug discovery and lead identification: Enables discovery of candidate compounds and assessment of bioactivity profiles for lead selection.
• Chemogenomics and target–compound relationship analysis: Facilitates chemogenomic analyses to relate compounds to targets and assess target druggability.
• Experimental model selection: Informs selection of suitable cell lines for functional and screening experiments.
• Hypothesis generation and translational research: Provides integrated evidence to generate hypotheses and support translational studies in cancer biology.

Methodology:

Consolidation and integration of publicly available biological annotations, chemical screening results, RNA interference screenings, gene expression data, amplification information, and 3D structural data; generation of target and compound synopses; provision of bioactivity views, chemogenomic analysis, and expression and protein interaction network exploration.