canSAR.ai: Accelerating Cancer Drug Discovery with AI-Powered Insights

canSAR.ai is a cutting-edge, publicly accessible knowledgebase designed for cancer drug discovery and translational research, integrating multidisciplinary data from biology, chemistry, pharmacology, structural biology, cellular networks, and clinical annotations. Powered by machine learning and artificial intelligence, it enables researchers to explore protein structures, druggable cavities, compounds, diseases, and clinical trials with advanced search capabilities, offering a comprehensive, single-portal solution for complex research questions. The platform is particularly valuable for scientists working in oncology, structural biology, and drug development, providing up-to-date, high-quality data with weekly updates to 3D structures and monthly enhancements to overall datasets.

Key Features:

• Integrated Multidisciplinary Data: Combines biological, chemical, pharmacological, structural, cellular, and clinical data for holistic cancer research.
• AI-Driven Druggability Predictions: Uses machine learning models (e.g., PocketSpy PU RF) to identify druggable cavities in protein structures.
• Advanced Search Capabilities: Supports searches across proteins, cell lines, drugs, compounds, diseases, 3D structures, and protein families using names, synonyms, keywords, or sequences.
• Chemical Search with Substructure & Similarity Matching: Employs the Symyx Cartridge for robust compound searches, including structure-based queries.
• Comprehensive 3D Structural Analysis: Features 229,748 3D structures, 746,848 PDB chains, and 1,696,848 PDB bioassemblies with detailed cavity assessments.
• Druggable Cavity Identification: Has identified 274,685 druggable cavities (4.01% of total) across PDB chains and 99,564 druggable structures (43.34% of total).
• Regular Updates: Weekly updates for 3D structural data and monthly updates for broader datasets ensure access to the latest research information.
• Public Access & No Licensing Required: Fully free to use for academic and commercial cancer researchers, promoting open science and collaboration.
• Clinical and Experimental Data Integration: Includes data from major sources like TCGA, TARGET, GTEx, cancer.gov, clinicaltrials.gov, PDB, and BindingDB.
• Expression & ADME Data: Offers expression profiles across tissues and cell lines, and ADME (absorption, distribution, metabolism, excretion) properties for compounds.
• Drug Combination Data Access: Allows downloading of synergistic drug combination data via the canSynergise Data link.
• Release Timeline Visualization: Tracks data growth and updates from 2010 to 2025, enhancing transparency and research tracking.
• Cross-Organism Search (Advanced Mode): Enables broader searches beyond human targets using the "Advanced Search" with "All organisms" filter.

Pricing: canSAR.ai is completely free to use for all researchers, with no licensing or subscription fees required.

Conclusion: canSAR.ai stands as a transformative, open-access platform for cancer drug discovery, combining vast, AI-analyzed datasets with powerful search tools to empower researchers worldwide in the fight against cancer.