PROTAC-PatentDB: A PROTAC Patent Compound Dataset
Revolutionizing PROTAC Drug Discovery with Patent Intelligence
The PROTAC-PatentDB, derived from rigorous patent literature analysis (2013-2023), comprises 63,136 unique PROTAC compounds across 590 patent families and 252 targets. This dataset significantly expands beyond existing public databases like PROTAC-DB and PROTACpedia, offering a richer chemical diversity for machine learning applications in targeted protein degradation. By predicting ADMET properties for all compounds, the PROTAC-PatentDB provides a robust foundation for computational drug discovery and AI-aided design.
Executive Impact: Key Achievements & Future Potential
This initiative significantly advances PROTAC research by providing an unparalleled dataset for AI-driven drug discovery, accelerating target identification and lead optimization.
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Unique PROTAC Compounds
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Patent Families
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Molecular Targets
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Patent Retrieval (Derwent Innovation)
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Filtering & Refinement (590 Families)
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Structure Extraction (SciFinder)
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Manual Curation (63,136 Compounds)
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ADMET Prediction (ADMETlab 3.0)
PROTAC-PatentDB vs. Other Databases
| Feature | PROTAC-PatentDB | PROTAC-DB 3.0 | PROTACpedia |
|---|---|---|---|
| Number of Compounds | 63,136 | 6,111 | 1,190 |
| Data Source | Patents | Articles | Articles |
| Chemical Space Coverage | Expanded, Diverse | Limited | Limited |
| ADMET Properties | Predicted for All | N/A | N/A |
Scale of PROTAC Compound Diversity
63,136 Unique PROTAC Compounds IdentifiedCase Study: IRAK4 PROTAC Discovery
The PROTAC-PatentDB dataset can be utilized across various stages of PROTAC drug discovery, as exemplified by the IRAK4-targeting PROTACs.
Structural Decomposition: Breaking down PROTACs into warheads, linkers, and E3 ligands for design insights.
Structural Optimization: Identifying frequently occurring substructures to guide design efforts.
Molecular Generation: Generating novel PROTAC candidates by combining existing fragments.
Hit Compound Screening: Using DeepPSA for synthetic accessibility prediction to prioritize compounds.
Calculate Your Potential ROI
Estimate the transformative impact of leveraging cutting-edge PROTAC data in your enterprise. Adjust the parameters below to see potential cost savings and efficiency gains.
Estimated Annual Savings
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Annual Hours Reclaimed
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Implementation Timeline
A phased approach ensures seamless integration and maximum impact within your existing R&D ecosystem.
Phase 1: Data Integration & Platform Setup
(2-4 Weeks)
Integrate PROTAC-PatentDB into existing computational pipelines and set up access to the online webserver. Training for research teams on data querying and utilization.
Phase 2: Initial AI/ML Model Training & Validation
(4-8 Weeks)
Utilize the expanded dataset to train and validate new AI/ML models for PROTAC design, ADMET prediction, and target interaction. Conduct pilot studies on selected targets.
Phase 3: Iterative Design & Optimization Cycles
(8-16 Weeks+)
Apply AI-driven insights from the PROTAC-PatentDB for iterative design and optimization of novel PROTAC molecules. Continuous feedback loop between computational predictions and experimental validation.
Ready to Transform Your PROTAC Discovery?
Connect with our experts to discuss how PROTAC-PatentDB can accelerate your research, enhance discovery pipelines, and drive innovation in targeted protein degradation.
Ready to Get Started?
Book Your Free Consultation.
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