CORE TECHNOLOGIES
The Science Powering PanAum
Three foundational technologies set PanAum apart from legacy computational chemistry platforms — each addressing a critical failure point in traditional drug discovery.
Generative Fusion Engine
A heterogeneous Graph Attention Network (GAT) that synthesizes multi-omics inputs to predict high-affinity molecular scaffolds. Goes beyond screening to true de novo molecular invention.
In-Loop ADMET Prediction
Safety and pharmacokinetic constraints are embedded directly into the molecule generation process — minimizing failure rates in pre-clinical and late-stage development.
Causal Target Validation
PanAum uses Mendelian Randomization to confirm that each target is a genuine driver of disease, not merely a downstream symptom — reducing costly late-stage attrition.
AlphaFold3 Structure Prediction
Integrated structural biology powered by AlphaFold3 identifies binding pockets and cavity geometries for precise generative molecular design inputs.
AI-Driven Drug Design vs. Traditional CADD
| Dimension | Traditional CADD | PanAum Platform |
|---|---|---|
| Target Discovery | Manual / Associative methods | Causal (Mendelian Randomization) |
| Molecule Design | Virtual library screening | Generative AI de novo design |
| ADMET Safety | Post-hoc discovery (late-stage) | In-loop prediction & constraint |
| Data Integration | Single-omics, siloed databases | Multi-omics, 6+ unified sources |
| Time to IND | 6–10 years | 18–36 months |
| Target Validation | Correlative evidence only | Causal, genetically validated |