01
Multi-Omics Integration
Whole Genome Sequencing, Proteomics, and Epigenomics layers combined for deep biological context.
AI-Driven Drug Discovery
From Unmet Medical Need to Optimized Clinical Candidate
PanAum fuses multi-omics integration, causal genetics, pharmacogenomics, and generative AI to accelerate the path from disease hypothesis to a population-characterized, IND-ready therapeutic candidate.
Multi-OmicsGenerative AIADMET In-LoopCausal GeneticsPharmacogenomics
panaum-cli
$ run_panaum --target "Target_X" --modality "SmallMolecule" ✓ AlphaFold3: Structure prediction complete ✓ Binding Pocket: 4 cavities identified ✓ PGx Profile: 6 metabolizer phenotypes mapped ✓ Generative AI: 1,402 scaffolds generated ✓ ADMET Filter: 12 compounds passed ✓ PGx Constraint: 9 compounds safe ✓ Top 3 leads ranked · CDx brief generated
14+
Pipeline Stages
10+
Data Sources Integrated
4×
Faster Than Manual Discovery
100%
Evidence-Backed Targets
Platform Overview
What PanAum Delivers
A unified computational ecosystem that bridges the gap between target identification and clinical-grade drug design. PanAum transforms static targets into dynamic, population-characterized therapeutic assets — multi-omics, network biology, pharmacogenomics, and generative chemistry in one automated workflow.
"The only end-to-end platform combining multi-source disease target intelligence, population-level pharmacogenomic profiling, and de novo generative molecular design in a single automated workflow."
02
Network Centrality Analysis
5 advanced metrics: Degree, Betweenness, Closeness, Eigenvector, and PageRank for target scoring.
03
Pharmacogenomics Layer
Population-level variant profiling built into every stage, from target selection to the final clinical report.
04
4 Therapeutic Modalities
Specialized workflows for Small Molecules, Antibodies, PROTACs, and Novel Target classes.
The Pipeline
End-to-End Automated 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.
1
Evidence Input
Integration of OpenTargets, GWAS, CTD, PharmGKB, and CPIC data to establish a high-confidence, pharmacogenomically-annotated evidence base.
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.
Competitive Differentiation
AI-Driven Drug Design vs. Traditional CADD
| Dimension | Traditional CADD | PanAum Platform |
|---|---|---|
| Target Discovery | Manual / Associative methods | Causal (Mendelian Randomization) + variant-risk scored |
| Molecule Design | Virtual library screening | Generative AI de novo design |
| ADMET Safety | Post-hoc discovery (late-stage) | In-loop prediction & constraint |
| Population Coverage | Assumed universal | Stratified across 5 ancestral groups via PGx layer |
| Metabolizer Safety | Discovered in Phase I/II | CYP/UGT polymorphism constraints in-loop at design stage |
| Data Integration | Single-omics, siloed databases | Multi-omics + pharmacogenomics, 10+ unified sources |
| Time to IND | 6–10 years | 18–36 months |
| Target Validation | Correlative evidence only | Causal, genetically validated |
| Clinical Output | Compound only | Lead + PGx biomarker + Companion Dx draft |
Core Technologies
The Science Powering PanAum
Five foundational technologies, each targeting a specific failure point in traditional drug discovery.
01
Generative Fusion Engine
A heterogeneous Graph Attention Network (GAT) that synthesizes multi-omics inputs to predict high-affinity molecular scaffolds, enabling genuine de novo molecular invention with pharmacogenomic variant constraints built directly into generation.
02
In-Loop ADMET Prediction
Safety and pharmacokinetic constraints are built directly into the molecule generation process, reducing failure rates in pre-clinical and late-stage development.
03
Causal Target Validation
PanAum uses Mendelian Randomization to confirm each target is a genuine driver of disease rather than a downstream symptom, cutting costly late-stage attrition.
04
AlphaFold3 Structure Prediction
Structural biology powered by AlphaFold3 identifies binding pockets and cavity geometries that feed into generative molecular design, including variant conformations from common missense polymorphisms.
05
Pharmacogenomics (PGx) Engine
Population-level variant intelligence from PharmGKB, CPIC, gnomAD, and the FDA Table of Pharmacogenomic Biomarkers runs through every pipeline stage, ensuring every lead is safe across real patient populations.
Who PanAum Serves
Built for Every Stage of Drug Discovery
From hypothesis validation to clinical-grade candidate delivery — PanAum fits the workflows of academic researchers, biotech startups, and global pharma teams.
Academic Researchers
Validate disease hypotheses with causal genetic evidence, generate optimized lead compounds, and receive a ready-to-publish pharmacogenomic variant landscape — all without a wet lab or clinical genetics team.
Biotech Startups
Compress discovery timelines from years to months. Enter investor conversations with an AI-generated, ADMET-filtered, PGx-characterized candidate and a Companion Diagnostic strategy.
Global Pharma Teams
Integrate PanAum into existing R&D pipelines for precision target intelligence, scalable molecular generation, and regulatory-ready PGx reporting. Reduce Phase I safety surprises.
Ready to Accelerate
From Hypothesis to IND-Ready Candidate in 18–36 Months
PanAum gives you the precision, speed, and scientific provenance needed to go from unmet medical need to a population-characterized, IND-ready therapeutic candidate.