NOD

NOD predicts novel therapeutic uses for existing drugs by identifying close and distant homologs of a target protein using a sequence-guided approach and mapping those homologs to compounds in the DrugBank database while expanding candidate chemical space via similarity searches.

Key Features:

• Sequence-Guided Homology Search: Employs a sensitive sequence-guided methodology to identify both close and distant homologs of a target protein.
• Integration with DrugBank: Maps identified protein homologs to compounds and associated annotations in the DrugBank database to suggest candidate drugs.
• Chemical Space Expansion: Expands potential candidates by performing similarity searches to explore related chemical structures beyond initial matches.
• Validation Performance: Validated against experimental and clinical reports, with a control study reporting NOD identified potential drug candidates more effectively than direct DrugBank searches in 65.6% of cases.

Scientific Applications:

• Drug repurposing prioritization: Prioritizes existing drugs for new therapeutic indications based on target protein homology and compound mappings.
• Target-centric candidate identification: Identifies candidate compounds for specific protein targets by leveraging evolutionary relationships and DrugBank annotations.
• Research support in pharmacology and bioinformatics: Supports customized investigations in pharmacology, bioinformatics, and related fields focused on discovering new drug-target hypotheses.

Methodology:

NOD performs sensitive sequence-guided homology searches to identify close and distant protein homologs, maps those homologs to DrugBank compounds, and expands candidate chemical space using similarity searches.