Pain-CKB

Pain-CKB aggregates chemogenomics data and computational tools to enable analysis of drug–target interactions and support multitarget discovery in pain regulation research.

Key Features:

• Comprehensive Database: Pain-CKB contains 272 market-available analgesics, 84 pain-related targets, 207 3D crystal or cryo-electron microscopy (cryo-EM) structures, and records for 234,662 chemical agents reported for target proteins.
• Integrated Computing Tools: Includes HTDocking for high-throughput docking simulations, TargetHunter for target identification, a BBB permeation predictor, NGL viewer for molecular visualization, and Spider Plot for data representation.
• Enhanced Chemogenomics Technology: Supports multiple compounds and multicavity proteins and provides customizable symbol displays to represent complex interactions within pain pathways.
• Bioactivity Determination: Employs revised bioactivity determination methods that avoid reliance on complex machine learning models to improve clarity of data interpretation.

Scientific Applications:

• Target identification and prioritization: Facilitates identification and prioritization of potential pain-related protein targets using TargetHunter and chemogenomics data.
• Multitarget drug discovery: Supports discovery of analgesics that modulate multiple targets by integrating docking (HTDocking), chemogenomics, and bioactivity data.
• Systems pharmacology and pathway analysis: Enables exploration of chemical molecules, genes, and proteins involved in pain regulation and examination of potential therapeutic combinations.

Methodology:

Integration of chemogenomics technology with HTDocking (high-throughput docking), TargetHunter, a BBB permeation predictor, NGL viewer, Spider Plot, and visualization/analysis algorithms, using 3D crystal and cryo-EM structures and revised bioactivity determination methods that avoid complex machine learning.