1
Evidence Input
Integration of OpenTargets, GWAS, CTD, PharmGKB, and CPIC data to establish a high-confidence, pharmacogenomically-annotated evidence base.
The PanAum Pipeline
End-to-End Automated Drug Discovery Workflow
PanAum's pipeline ensures every step flows logically from initial evidence collection to a final executive-ready report, with no manual handoffs and full scientific provenance at each stage. The PGx layer runs as an integrated track with bidirectional data exchange throughout.
2
Network & Enrichment
Building the biological framework via STRING network construction and pathway enrichment analysis, with drug-response SNPs overlaid on the target network.
3
Prioritization
Determining the most promising therapeutic nodes via hub-gene scoring, multi-criteria ranking, and population-level allele frequency stratification across global ancestries.
4
Optimization
Enhancing drug candidate quality through target annotation, variant-aware druggability screening across polymorphic protein conformations, and known-drug comparisons.
5
Generative Design
De novo scaffold generation using the Generative Fusion Engine with in-loop ADMET filtering and CYP/UGT/transporter polymorphism constraints embedded directly into the generation process.
6
Output & Reporting
Consolidating intelligence into structured final reports, executive summaries, PGx biomarker profiles, and Companion Diagnostic briefs for clinical decision-making.
Integrated PGx Track. The Pharmacogenomics layer runs alongside every stage with bidirectional data exchange — population-level variant intelligence informs target scoring, scaffold generation, ADMET constraints, and the final Companion Diagnostic brief simultaneously.
Want the Deeper Science?
Read about the Generative Fusion Engine, In-Loop ADMET, Causal Target Validation, AlphaFold3 integration, and the Pharmacogenomics (PGx) Engine.
Core Technologies →