Launch Announcement Drafts¶

Ready-to-post drafts for LinkedIn, Twitter/X, and Hacker News. Edit to your voice before posting.

LinkedIn Post¶

I've been working on this for 14 years. Today I'm giving it away.

In 2012, I set out to use my high-performance computing background for something that mattered. I started with one conviction: no parent should ever have to lose a child to disease.

That conviction led me to Pediatric Neuroblastoma. I taught myself biology, genomics, molecular pathways, drug discovery -- whatever the work required. I made one commitment early: I would not profit from this.

Today I'm releasing the HCLS AI Factory -- an open-source platform that takes raw patient DNA and produces ranked drug candidates in under 5 hours. On a single desktop workstation.

What it does:

Takes raw DNA sequencing data (~200 GB) and runs GPU-accelerated variant calling (NVIDIA Parabricks) -- 11.7 million variants in 2-4 hours instead of days
Annotates variants against ClinVar, AlphaMissense, and a curated knowledge base of 201 genes across 13 therapeutic areas
Uses RAG-grounded AI reasoning (Milvus + Claude) to identify druggable gene targets
Generates 100 novel drug candidates via BioNeMo (MolMIM + DiffDock) in under 16 minutes
Three domain-specific intelligence agents (CAR-T, Medical Imaging, Precision Oncology) with 1,296 tests

The hardware: an NVIDIA DGX Spark. $3,999. Sits on a desk.

The traditional path from patient DNA to drug candidates takes 6-18 months and $50K-500K+ in infrastructure. This does it in an afternoon.

Everything is Apache 2.0. No vendor lock-in. No strings attached.

My hope is simple: a family sitting in a hospital room gets access to the same computational tools that used to require a research institution. A graduate student at any university can run a complete precision medicine pipeline on hardware they can actually afford. A startup can prototype therapeutic hypotheses without a six-figure compute budget.

If you work in genomics, drug discovery, or clinical research -- take it. Use it. Make it better.

GitHub: https://github.com/ajones1923/hcls-ai-factory Docs: https://hcls-ai-factory.org

PrecisionMedicine #OpenSource #Genomics #DrugDiscovery #NVIDIA #DGXSpark #AI #Bioinformatics #Apache2¶

Twitter/X Thread¶

Tweet 1 (hook):

I spent 14 years building this. Today I'm releasing it for free.

An open-source platform that goes from raw patient DNA to ranked drug candidates in under 5 hours.

On a $3,999 desktop workstation.

Apache 2.0. No strings attached.

Thread:

Tweet 2 (what it does):

The HCLS AI Factory is a 3-stage pipeline:

Stage 1: GPU-accelerated genomics (Parabricks) -- 11.7M variants in 2-4 hrs Stage 2: RAG-grounded target ID (Milvus + Claude) -- <5 sec per query Stage 3: AI drug discovery (MolMIM + DiffDock) -- 100 candidates in 16 min

All on one NVIDIA DGX Spark.

Tweet 3 (the numbers):

Key numbers:

3.56M searchable vectors in Milvus
201 genes across 13 therapeutic areas
171 druggable targets (85%)
1,296 agent tests in 3.78 sec
Top drug candidate: +39% improvement over seed compound

Traditional approach: 6-18 months, $50K-500K+ This: <5 hours, $3,999

Tweet 4 (intelligence agents):

It includes 3 domain-specific intelligence agents:

CAR-T: cell therapy evidence + comparative analysis
Imaging: NVIDIA NIM workflows (VISTA-3D, MAISI) + FHIR R4
Precision Oncology: MTB packets, trial matching, therapy ranking

Each connects to a shared genomic evidence base (3.5M vectors).

Tweet 5 (why):

I started this because of Pediatric Neuroblastoma.

My hope: a family in a hospital room gets access to the same tools that used to require a research institution. A grad student can run precision medicine on hardware they can afford.

Take it. Use it. Make it better.

Tweet 6 (links):

GitHub: https://github.com/ajones1923/hcls-ai-factory Docs: https://hcls-ai-factory.org Demo video: [link]

Apache 2.0 -- free for commercial and non-commercial use.

Built with: NVIDIA Parabricks, BioNeMo, Milvus, Anthropic Claude, RDKit

Hacker News (Show HN)¶

Title: Show HN: Open-source platform -- Patient DNA to drug candidates in 5 hours on a $4K workstation

Body:

I've been working on this for 14 years, starting from a HPC background and teaching myself genomics along the way. The HCLS AI Factory is an end-to-end precision medicine platform that processes raw DNA sequencing data into ranked novel drug candidates -- entirely on a single NVIDIA DGX Spark ($3,999).

Three stages:

GPU genomics (NVIDIA Parabricks): 200GB FASTQ -> 11.7M variant calls in 2-4 hours (vs 1-2 days on CPU)
RAG-grounded target identification (Milvus + Claude): 3.56M vectors, 201 genes across 13 therapeutic areas, <5 sec per query
AI drug discovery (BioNeMo MolMIM + DiffDock + RDKit): 100 ranked drug candidates in 8-16 minutes

The platform also includes three domain-specific intelligence agents (CAR-T cell therapy, medical imaging, precision oncology) with a combined 1,296 tests.

Traditional path: 6-18 months and $50K-500K+ in infrastructure. This does it in an afternoon on a desktop.

Everything is Apache 2.0. The demo targets VCP/Frontotemporal Dementia and produces candidates with +39% composite improvement over the clinical seed compound.

GitHub: https://github.com/ajones1923/hcls-ai-factory

Tech stack: NVIDIA Parabricks 4.6, Milvus 2.4, BGE-small-en-v1.5, Anthropic Claude, BioNeMo MolMIM/DiffDock, RDKit, Nextflow, Docker, Streamlit

Happy to answer questions about the architecture, benchmarks, or the biology.

Reddit (r/bioinformatics, r/genomics, r/MachineLearning)¶

Title: I open-sourced a platform that goes from raw patient DNA to drug candidates in <5 hours on a $4K workstation (Apache 2.0)

Body:

After 14 years of work, I'm releasing the HCLS AI Factory -- an end-to-end precision medicine platform that runs entirely on a single NVIDIA DGX Spark ($3,999).

What it does:

Stage 1: GPU-accelerated variant calling via Parabricks (11.7M variants from 30x WGS in 2-4 hours)
Stage 2: RAG pipeline with Milvus (3.56M vectors), ClinVar, AlphaMissense, and Claude for druggable target identification
Stage 3: Generative drug discovery via BioNeMo MolMIM + DiffDock + RDKit (100 ranked candidates in 8-16 min)

Also includes:

3 domain-specific agents (CAR-T, Imaging, Oncology) with 1,296 tests
201 genes across 13 therapeutic areas, 85% druggable
Synthetic demo VCF for quick evaluation
One-command quickstart: ./quickstart.sh

Why it matters:

The traditional path from patient DNA to a drug lead takes 6-18 months and requires significant infrastructure investment. This brings the entire workflow to a desktop workstation that anyone can afford.

Apache 2.0 -- use it for research, commercial work, whatever you need.

GitHub: https://github.com/ajones1923/hcls-ai-factory Docs: https://hcls-ai-factory.org

Happy to discuss architecture decisions, benchmark methodology, or answer questions.