Use cases — what the network computes

Gennode is built for deep bio & scientific data — the workloads that need the most compute, the largest datasets and the strongest privacy. Whatever the field, the common thread is the same: huge compute + large, sensitive data + a hard privacy requirement.

These are the target workloads — real compute jobs roll out per the Roadmap.

🧬 Genomics & DNA

• Sequence alignment & genome assembly
• Variant calling (SNPs, indels, structural variants)
• Whole-genome / exome interpretation
• Rare-disease variant analysis
• Pharmacogenomics — drug-response variants
• Polygenic risk scores and population genetics

🧪 Proteins & structure

• Protein folding & structure prediction (AlphaFold / Boltz-style)
• Protein–protein interaction
• Binding-site & function prediction

💊 Drug discovery

• Molecular docking & virtual screening
• ADMET / toxicity prediction
• Generative molecule design

🔬 Multi-omics & research

• Transcriptomics, epigenomics, microbiome, metabolomics
• Large-scale biological simulations
• Disease & health research at scale
• Training & serving bio models — including Gennode's bio model

🤖 AI models for genomics & protein analysis

• Running AI models that analyze genomes, variants and multi-omics data
• Distributed training & fine-tuning of bio models (protein folding, genomics)
• Serving the purpose-built Gennode bio model — built from scratch if needed

Each of these needs enormous GPU time and terabytes of sensitive data. That is exactly what a private, decentralized network is for. See The network and the Data layer.