Unspoken Problem in Preclinical and Clinical Development
Most drug programs fail not because of poor science, but due to unseen risks and inefficiencies that current tools fail to address.
Fragile Inputs
Drug data are precious, expensive, and tricky for conventional deep learning
Domain Limitations
Models winning in Big Data industries fall short on patient statistics
Model Constraints
Ultra-scale models do not solve the problem. They overfit.
Algorithmic Solutions
Driven by data-efficient algorithms, our predictive intelligence solve the problem for drugs and for patients.
Deep Learning Mechanics: the Engine Behind Ultra Productivity
Unprecedented Predictive Power Driven by Generalizability
Our predictive intelligence works in a way new to drug developers:
- Prediction does not rely on chemical similarity
- Target structures and drug actions are not taken into account
It also brings two important advantages:
- Removal of limits on chemical scaffolds
- Obviation of the need for target disclosure
Highest correlation and lowest error
(Nat Machine Intell 2024)
(Nat Machine Intell 2024)
Unlimited Scaffold Exploration
- Built on millions of compounds, comprising 27,000+ scaffolds, over a broad spectrum of Lipinski properties
- Can be applied to any structural series for broader, deeper exploration of the chemical space
Coverage of Lipinski properties and chemical scaffolds, the latter expressed as numbers and combinations of ring types. Plot interaction enabled. Click and drag to rotate. ↓
Versatile Implementation
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- Compatible with structures for drug repositioning, derived from natural products, and from de novo design
- Can be adopted in addition to AI molecular design and other existing workflows
