01
Multi-Omics Integration
Whole Genome Sequencing, Proteomics, and Epigenomics layers combined for deep biological context.
MULTI-OMICSGENERATIVE AIADMET IN-LOOPCAUSAL GENETICS
From Unmet Medical Need to Optimized Clinical Candidate
PanAum fuses multi-omics integration, causal genetics, and generative AI to accelerate the path from disease hypothesis to an IND-ready therapeutic candidate.
panaum-cli
$ run_panaum --target "Target_X" --modality "SmallMolecule" ✓ AlphaFold3: Structure prediction complete ✓ Binding Pocket: 4 cavities identified ✓ Generative AI: 1,402 scaffolds generated ✓ ADMET Filter: 12 compounds passed safety thresholds ✓ Lead Optimization: Top 3 leads ranked for synthesis
12+
Pipeline Stages
6+
Data Sources Integrated
4×
Faster Than Manual Discovery
100%
Evidence-Backed Targets
02 — 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 therapeutic assets — bringing together the best of multi-omics, network biology, and generative chemistry under one automated roof.
“PanAum is the only end-to-end platform that combines multi-source disease target intelligence with 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
4 Therapeutic Modalities
Specialized workflows for Small Molecules, Antibodies, PROTACs, and Novel Target classes.
03 — 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.
- 01
Evidence Input
Integration of OpenTargets, GWAS, and CTD data to establish a high-confidence evidence base.
- 02
Network & Enrichment
Building the biological framework via STRING network construction and pathway enrichment analysis.
- 03
Prioritization
Determining the most promising therapeutic nodes via hub-gene scoring and final multi-criteria ranking.
- 04
Optimization
Enhancing drug candidate quality through target annotation, druggability screening, and known-drug comparisons.
- 05
Generative Design
De novo scaffold generation using the Generative Fusion Engine with in-loop ADMET filtering.
- 06
Output & Reporting
Consolidating intelligence into structured final reports and executive summaries for clinical decision-making.
04 — COMPETITIVE DIFFERENTIATION
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 |
05 — 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.
06 — WHO PANAUM SERVES
Built for Every Stage of Drug Discovery
Academic Researchers
Validate disease hypotheses with causal genetic evidence and generate optimized lead compounds — without a wet lab.
Biotech Startups
Compress discovery timelines from years to months. Move faster with AI-generated, ADMET-filtered candidates ready for synthesis.
Global Pharma Teams
Integrate PanAum into existing R&D pipelines for precision target intelligence and scalable molecular generation.
Ready to Accelerate Your Drug Discovery Program?
PanAum provides the precision, speed, and scientific provenance required to succeed — from unmet medical need to IND-ready candidate.