Find the Right Molecule. Faster Than Biology Allows.
Zabrizon's AI drug discovery platform combines generative chemistry, predictive ADMET modelling, and multimodal biological data analysis to compress early drug development timelines and improve candidate quality.
Drug Discovery AI Capabilities
Generative chemistry, predictive biology, and multimodal data science — purpose-built for pharmaceutical R&D.
Generative Molecular Design
Deep learning models generate novel drug-like molecules with optimised properties — beyond what traditional medicinal chemistry libraries contain.
- Graph neural network and transformer-based molecule generation
- Multi-parameter optimisation: potency, selectivity, and ADMET
- De novo scaffold design for novel IP space
- Synthesisability scoring and synthetic route prediction
Target Identification & Validation
AI analysis of genomics, proteomics, and disease biology datasets to identify and prioritise novel therapeutic targets with high disease relevance and tractability.
- Multi-omics data integration: genomics, transcriptomics, proteomics
- Disease pathway analysis and target druggability scoring
- Phenotypic screening data pattern recognition
- Competitive intelligence on target class and modality landscape
ADMET & Toxicity Prediction
Computational prediction of absorption, distribution, metabolism, excretion, and toxicity properties — enabling early elimination of liabilities before expensive wet-lab testing.
- In silico ADMET prediction: solubility, permeability, CYP, hERG
- Organ toxicity prediction models trained on clinical data
- Structural alert identification and SAR analysis
- Regulatory-grade toxicity report generation for IND submissions
Virtual Screening & Hit Identification
Structure-based and ligand-based virtual screening of billions of commercially available and synthesised compounds — identifying hits 100× faster than physical HTS.
- Ultra-large virtual library screening (billions of compounds)
- Docking and pharmacophore-based hit identification
- Activity cliff and SAR landscape mapping
- Experimental data integration for model retraining
AI-Accelerated Drug Discovery Pipeline
AI compresses every stage from disease hypothesis to IND-ready candidate.
Target ID
AI multi-omics analysis to identify and rank novel therapeutic targets
Hit Discovery
Virtual screening of billions of compounds for target binding
Lead Optimisation
Generative AI design and ADMET prediction to refine candidates
Preclinical
Toxicity prediction and in silico modelling to reduce animal testing
IND Filing
Automated regulatory package assembly for FDA IND submission
Data Standards & Computational Framework
Built on open scientific standards and validated computational methods.
Ready to Bring AI Into Your Drug Discovery Pipeline?
See how Zabrizon's computational chemistry and ML platform can identify better candidates faster.
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