NERDD

NERDD predicts small-molecule metabolism, flags assay-interference risks, and identifies natural products to support drug discovery research.

Key Features:

• Metabolism Prediction: FAME predicts sites of metabolism in small organic molecules and GLORY identifies potential metabolites formed during phase 1 and phase 2 metabolic processes.
• Assay Interference Detection: Hit Dexter flags compounds that may interfere with biological assays, reducing false positives and false negatives caused by compound interference.
• Natural Product Identification: NP-Scout identifies natural products and their derivatives within large compound libraries to support exploration of biologically active lead structures.
• Development of Additional Models: The platform includes several models and components currently under development to expand capabilities for drug discovery research.

Scientific Applications:

• Compound Screening and Preclinical Testing: Supports stages from initial compound screening to preclinical testing by providing computational predictions for prioritization.
• Lead Optimization: Metabolism and assay-interference predictions inform optimization of lead compounds for efficacy and safety.
• Natural Product Discovery: Natural product identification accelerates discovery of novel therapeutic agents and lead structure development.

Methodology:

Uses advanced computational algorithms to analyze chemical structures and predict biological interactions and metabolic pathways, employing in silico high-throughput screening and analysis.