DsecOS Enterprise

AI-Powered Drug Discovery Platform

From Molecule to Model in Minutes

Accelerated R&D Secure Scalable AI FDA Ready

2

Platform Overview

DsecOS Enterprise provides the secure, high-performance platform for an AI-driven drug discovery pipeline. It integrates AlphaFold 3, molecular docking (AutoDock Vina), and generative chemistry models to screen millions of compounds against target proteins — all within a zero-trust, compliant environment.

Screening Speed

10,000x

Faster than wet-lab

Cost Reduction

90%

Vs. physical assays

Deployment Time

<20 min

Full cluster

R&D Timeline

Years → Months

Accelerated

                Business Value
                Reduce R&D Timeline: From years to months
Cut Screening Costs: 90% reduction vs. physical assays
Secure Collaboration: Global research teams with IP protection
Regulatory-Ready: FDA/EMA compliant from day one

            

⚡ Designed For: Biotech and pharmaceutical companies requiring secure, scalable AI infrastructure for drug discovery

3

Technical Foundation

Component	Role	Security Features
AlphaFold 3	Protein structure prediction	GPU-bound, rootless, seccomp-restricted
AutoDock Vina	Molecular docking & binding affinity	Isolated per-job containers
Generative AI (ChemBERTa)	De novo molecule generation	Encrypted model weights
PostgreSQL + Vector DB	Compound + result storage	Row-level security, encrypted at rest

                Platform Security
                GPU Isolation: PCI passthrough + NVIDIA container toolkit with SELinux gpu_t
Data Security: LUKS-encrypted Ceph volumes for proprietary compound libraries
Orchestration: LXC containers with resource quotas for parallel docking jobs
Compliance: Immutable logs + tamper-proof audit trails

            

4

Cluster Architecture

8-Node GPU Cluster (On-Prem or Hybrid)

graph TD subgraph "DsecOS Enterprise Cluster (8 Nodes)" N1[DsecOS Node 1
Master + Ceph MON] N2[DsecOS Node 2
AlphaFold GPU] N3[DsecOS Node 3
Docking Worker GPU] N4[DsecOS Node 4
AI Generator GPU] N5[DsecOS Node 5
Ceph OSD + NVMe] N6[DsecOS Node 6
Ceph OSD + NVMe] N7[DsecOS Node 7
Ceph OSD + NVMe] N8[DsecOS Node 8
Jupyter + Results] end subgraph "AI Drug Discovery Pipeline" AF["AlphaFold 3
(Structure Prediction)"] DOCK["AutoDock Vina
(Docking Engine)"] GEN["ChemBERTa
(Molecule Generator)"] JUP["JupyterLab
(Orchestration)"] end subgraph "Data & Compliance" DB["PostgreSQL + PGVector
(Compounds + Embeddings)"] AI["AI Scorer
(Binding Prediction)"] LIC[License Server] end N1 <-->|Corosync HA| N2 N2 <--> N3 N3 <--> N4 N1 --> CEPH[Ceph Cluster
Encrypted NVMe Pool] AF --> N2 DOCK --> N3 GEN --> N4 JUP --> N8 DB --> N5 AI --> N6 CEPH --> AF CEPH --> DB CEPH --> GEN AI --> DOCK LIC --> N1 style N1 fill:#121212,stroke:#00D9A5,color:#FFF style AF fill:#1E1E1E,stroke:#00D9A5,color:#FFF style AI fill:#8B0000,color:#FFF

5

Drug Discovery Pipeline Flow

journey title AI Drug Discovery Pipeline Flow section Cluster Setup Activate Pharma License: 5: R&D Director PXE Deploy 8 GPU Nodes: 5: BioIT Enable GPU Passthrough: 4: Auto-Ansible section Pipeline Launch Upload Target Protein PDB: 5: JupyterLab Run AlphaFold Prediction: 5: One-Click Generate 1M Candidates: 4: ChemBERTa section Screening Parallel Docking 100k per hour: 5: AutoDock AI Rank Binding Affinity: 4: ML Scorer Select Top 100 Hits: 4: Visualization section Validation Export for Wet Lab: 3: Secure PDF Generate FDA Audit Log: 5: Compliance Officer Archive to Immutable Storage: 5: Ceph Snapshots

6

Deployment Requirements

⚡ Prerequisites: DsecOS Enterprise Pharma Edition license, 8x servers (128 GB RAM, 2x NVIDIA A100, 4 TB NVMe), 100 Gbps network

Step 1: Provision Cluster

/scripts/pxe-deploy.sh --cluster drug-discovery --nodes 8 --gpu a100 --ceph-tier nvme

Step 2: Deploy Custom Stack

Create /templates/stacks/drug-discovery.yml:

version: '3.8'
services:
  alphafold:
    image: ghcr.io/deepmind/alphafold:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    volumes:
      - ceph-proteins:/data
    command: --fasta_paths=/data/target.fasta --output_dir=/data/output

  docking:
    image: gnina/gnina:latest
    volumes:
      - ceph-library:/library
    command: --receptor protein.pdbqt --ligand batch.sdf --autobox_ligand

  generator:
    image: moseleybioinformatics-lab/chemmodel:latest
    volumes:
      - ceph-models:/models
    command: generate --target smiles --count 1000000

  jupyter:
    image: jupyter/scipy-notebook
    ports:
      - "8888:8888"
    volumes:
      - ceph-notebooks:/home/jovyan

volumes:
  ceph-proteins:
    driver: cephfs
  ceph-library:
    driver: cephfs
  ceph-models:
    driver: cephfs
  ceph-notebooks:
    driver: cephfs

Deploy Command

dsecos deploy drug-discovery

7

Run Pipeline

In JupyterLab (https://your-ip:8888):

# Predict structure
!alphafold --fasta target.fasta

# Screen 1M compounds
!gnina --receptor output.pdbqt --ligand library.sdf --exhaustiveness 16

# AI scoring
results = ai_model.predict(docked_poses)
top_hits = results.nlargest(100, 'binding_affinity')

                Export & Compliance
                FDA-Ready Report: Generate via UI with full audit trail
WORM Storage: Archive to immutable Ceph bucket
Regulatory Compliance: 21 CFR Part 11, GxP-ready

            

8

Security & Compliance

IP Protection

100%

Model weights encrypted

Audit Trail

Crypto Hash

Every job logged

Compliance

21 CFR

Part 11, GxP-ready

Encryption Overhead

<1%

Minimal impact

                Security Features
                IP Protection: Model weights encrypted; SELinux blocks exfiltration
Audit Trail: Every job logged with cryptographic hash
Compliance: 21 CFR Part 11, GxP-ready for FDA/EMA submissions
Data Sovereignty: All data encrypted at rest and in transit

            

9

Performance Metrics

Metric	DsecOS Platform	Traditional Methods	Advantage
Structure Prediction	2.1 hours	3 days (CPU)	34x faster
Docking Speed	120,000 ligands/hour	1,200/hour	100x faster
Hit Rate Improvement	+18%	Baseline	AI scoring
Screening Speed	10,000x	Wet-lab baseline	Massive acceleration

2.1h

Structure Prediction

120K/hr

Docking Speed

+18%

Hit Rate

<1%

Encryption Overhead

10

Return on Investment

                Mid-Sized Pharma Example (12 Targets/Year)
                
                            Category
                            Current Costs
                            With DsecOS
                            Savings
                        
                            Annual Operating Costs
                            $8,400,000
                            $1,200,000
                            $7,200,000
                        
                            Time to Clinic
                            Baseline
                            6 months faster
                            Accelerated
                        
                            Screening Efficiency
                            Traditional methods
                            10,000x faster
                            Massive improvement

Category	Current Costs	With DsecOS	Savings
Annual Operating Costs	$8,400,000	$1,200,000	$7,200,000
Time to Clinic	Baseline	6 months faster	Accelerated
Screening Efficiency	Traditional methods	10,000x faster	Massive improvement

Total Annual Savings

$7,200,000+

Plus 6 months faster to clinic

Accelerated Pharmaceutical R&D

10,000x

Faster

90%

Cost Cut

$7.2M

Savings

FDA

Ready

"From Molecule to Model in Minutes"

SECURE SCALABLE COMPLIANT AI-POWERED

DsecOS Enterprise Pharma Edition