What did Paul Conyngham actually do?

Conyngham, a 17-year ML veteran running Sydney consultancy Core Intelligence, sequenced his dog Rosie's tumor DNA, used ChatGPT for research planning and treatment strategy, AlphaFold to model mutated protein structures, and his own ML algorithms for neoantigen selection. The resulting mRNA sequence was produced by UNSW's RNA Institute and administered by University of Queensland. Rosie's tumor shrank 75% within a month.

What tools did he use and are they free?

ChatGPT (paid, ~$20/month for Plus) for research planning. AlphaFold (free, open-source from Google DeepMind — runs locally or via Google Colab) for protein structure prediction. Custom Python ML for neoantigen ranking (replicable with open-source tools). DNA sequencing cost ~$3,000 via a commercial genomics lab. The mRNA vaccine production required a university lab — that part isn't DIY-able from home.

What computing resources does AlphaFold require?

AlphaFold 2 needs a GPU with 8GB+ VRAM (RTX 3070 or better) and 64GB RAM for full runs. Google Colab (free tier) can run lighter AlphaFold predictions. AlphaFold 3 is web-based via Google's AlphaFold Server — no local GPU needed for single protein predictions. For serious genomics pipeline work, a cloud A100 instance (~$2–3/hr on Lambda Labs or RunPod) handles the full workflow.

What is neoantigen selection and why does it matter?

Neoantigens are mutated proteins unique to cancer cells — they don't appear on healthy cells. A vaccine that trains the immune system to recognize neoantigens targets cancer specifically without attacking healthy tissue. Identifying which of hundreds of mutations produces the best neoantigen candidates is the ML step in the pipeline — ranking mutations by predicted immunogenicity, MHC binding affinity, and protein stability.

Could you do this with OpenClaw?

The research planning, literature review, pipeline coordination, and data analysis steps are all OpenClaw territory — running bioinformatics tools, interpreting AlphaFold outputs, cross-referencing mutation databases (ClinVar, UniProt, cBioPortal), drafting the vaccine sequence spec. The wet lab steps (DNA sequencing, mRNA synthesis) require physical infrastructure OpenClaw can help you find and coordinate with, but can't replace.

What is IsoDDE and how does it improve on AlphaFold for this pipeline?

IsoDDE is Isomorphic Labs' Drug Design Engine (February 2026). It more than doubles AlphaFold 3's accuracy on novel protein-ligand structure prediction, is 19.8x better on antibody-antigen interfaces, outperforms gold-standard physics-based methods on binding affinity, and identifies cryptic binding pockets that labs take six months to find manually. It directly upgrades Step 3 of Conyngham's pipeline — protein structure and druggable surface identification — though it's not yet publicly available as a free tool.

Is this proven science or one lucky anecdote?

Both, honestly. The institutions are real (UNSW RNA Institute, University of Queensland), the tumor response is documented, and the approach — personalized mRNA neoantigen vaccines — is the same one Moderna and BioNTech are pursuing for human cancer. But it's n=1, no controls, unknown confounders. Scientists involved are cautiously excited but stress it needs controlled trials. It's a proof of concept, not a clinical result.

One Man, $3,000, and an AI Pipeline: How Paul Conyngham Designed a Custom Cancer Vaccine for His Dog

By Prahlad Menon Published 2026-03-15 9 min read

In December 2025, a Sydney tech entrepreneur drove 10 hours to give his rescue dog her first injection of a cancer vaccine he’d designed himself using ChatGPT, AlphaFold, and his own machine learning code. A month later, the tumor had shrunk 75%. The genomics professor who helped him called it “the first personalized cancer vaccine ever designed for a dog.”

This is the story of how Paul Conyngham did it — the exact tools, the exact pipeline, and what it would take for you to replicate it.

The situation

Rosie, an eight-year-old Staffordshire Bull Terrier–Shar Pei cross, was diagnosed with mast cell cancer in 2024 after large tumors appeared on one of her hind legs. Mast cell cancer is the most common skin cancer in dogs — aggressive, usually incurable through conventional treatment.

Conyngham tried everything: multiple surgeries, chemotherapy, immunotherapy. The chemo slowed the spread but didn’t shrink the tumors. Vets gave Rosie one to six months to live.

Conyngham is a 17-year veteran of machine learning and data science who runs Sydney consultancy Core Intelligence. He turned to the tools he knew.

The pipeline: exactly what he used

Step 1: DNA sequencing (~$3,000)

Rosie’s tumor tissue was sent to a commercial genomics lab for whole-exome sequencing — comparing the tumor’s DNA to her healthy cells to identify somatic mutations: the specific DNA changes that exist in the cancer but not in normal tissue. This is the raw data the entire pipeline depends on.

Cost: ~$3,000 AUD for tumor + matched normal sequencing. DIY-able? Yes — commercial labs (Illumina partners, GeneDx, Dante Labs internationally) offer whole-exome sequencing. You send tissue, they return FASTQ files.

Step 2: ChatGPT — research planning and pipeline design

Conyngham used ChatGPT throughout — not as a medical device, but as a research collaborator. He used it to:

Map the treatment strategy: understand the immunotherapy landscape, identify personalized mRNA neoantigen vaccines as the target approach
Plan the sequencing pipeline: what data to order, what format, what tools to apply downstream
Interpret genomic outputs: cross-reference mutations against known oncogenes, understand which proteins were affected
Iterate on the vaccine design: refine the mRNA sequence specification

“ChatGPT assisted throughout that entire process.” — Paul Conyngham

Cost: ChatGPT Plus, ~$20/month. Computing requirements: None beyond a web browser.

Step 3: AlphaFold — protein structure prediction

For the mutations identified in the sequencing data, Conyngham used Google DeepMind’s AlphaFold to model the 3D structures of the mutated proteins. This is critical: a mutation that changes a protein’s shape in a specific way may expose a surface that the immune system can target. AlphaFold predicts what that shape actually looks like.

AlphaFold options:

Option	Cost	GPU needed	Best for
AlphaFold Server (Google)	Free	None — web-based	Single protein predictions
AlphaFold 2 (local)	Free, open-source	8GB+ VRAM, 64GB RAM	Full pipeline control
Google Colab	Free (limited) / $10/mo	Cloud A100	Medium-scale runs
Cloud GPU (Lambda/RunPod)	~$2–3/hr	A100 80GB	Full dataset runs

For a small set of candidate proteins (dozens, not thousands), the free AlphaFold Server handles it. For processing hundreds of mutation candidates programmatically, you need local GPU or cloud compute.

Computing requirements for local AlphaFold 2:

GPU: NVIDIA with 8GB+ VRAM (RTX 3070 minimum; A100 recommended for speed)
RAM: 64GB system RAM
Storage: ~2.5TB for databases (can be reduced with smaller database versions)
OS: Linux

Step 4: Custom ML — neoantigen selection

This is the step that required Conyngham’s professional ML background. From hundreds of somatic mutations, you need to identify which ones produce the best neoantigen candidates — mutated peptides that:

Are presented on the tumor cell’s surface via MHC molecules (detectable by the immune system)
Are unique to the tumor (won’t cause autoimmune response against healthy tissue)
Are predicted to trigger a strong T-cell response

Conyngham wrote his own neoantigen ranking algorithm. But the tools to replicate this are open-source:

pVACtools — open-source neoantigen identification pipeline from Washington University, specifically designed for personalized cancer vaccines
NetMHCpan — MHC binding affinity prediction
IEDB tools — immunogenicity prediction
MuTect2 (GATK) — somatic mutation calling from sequencing data

Combined, these form the standard academic neoantigen pipeline. They run on a Linux machine with 16–32GB RAM and a modern CPU. No GPU required for most steps.

Step 5: The mRNA sequence specification

The output of all four steps above is a half-page document: the specific mRNA sequence encoding the selected neoantigens, in a format a lab can synthesize. This is what Conyngham brought to UNSW.

Output format: FASTA or similar sequence specification. Computing requirements: None — it’s text.

Step 6: mRNA synthesis (requires a lab)

Páll Thordarson, director of the UNSW RNA Institute, took Conyngham’s sequence and produced the physical mRNA vaccine — formulated in lipid nanoparticles for delivery. This took less than two months.

This step cannot be done at home. It requires:

RNA synthesis equipment (~$100K+)
Clean room conditions
Lipid nanoparticle formulation capability
Qualified researchers and regulatory compliance

What you need: A relationship with a university RNA lab or biotech CRO willing to collaborate. Conyngham got this through persistence — cold-contacting researchers and getting introductions. The UNSW team initially hesitated but agreed after reviewing his analysis.

Step 7: Ethics approval and administration

Animal ethics approval took three months — longer than designing the vaccine. Vaccine was administered at University of Queensland in December 2025 by Dr. Rachel Allavena.

Lesson: The regulatory step is the longest part of the whole process.

How you could do this with OpenClaw

The compute-heavy and wet-lab steps require dedicated resources, but a significant portion of this pipeline is coordination, analysis, and research — exactly what an AI agent is suited for.

What OpenClaw can do in this pipeline:

Research coordination — Search literature (PubMed, bioRxiv), identify researchers working on relevant problems, draft cold-contact emails to university labs
Genomic data analysis — Run bioinformatics tools via shell commands: GATK MuTect2 for somatic mutation calling, pVACtools for neoantigen prediction, annotate variants against databases (ClinVar, UniProt, cBioPortal)
AlphaFold orchestration — Submit sequences to AlphaFold Server API or run local AlphaFold via Docker, parse structure outputs, compare mutant vs. wildtype protein conformations
Neoantigen ranking — Run NetMHCpan predictions across candidate peptides, rank by predicted binding affinity and immunogenicity score, filter by uniqueness to tumor
Vaccine sequence specification — Generate the mRNA sequence document in the format required for synthesis
Ethics application drafting — Research the relevant animal ethics committee, draft the application, track submission status

A realistic OpenClaw setup for this:

openclaw: "Analyze the somatic mutations in rosie_tumor_variants.vcf.
Filter for non-synonymous coding mutations. Run pVACtools on the top 50 
candidates. Submit the 10 best neoantigen peptides to NetMHCpan for MHC 
binding prediction. Return ranked candidates with scores."

What OpenClaw can’t replace: The physical sequencing, the mRNA synthesis, the wet lab, and the regulatory process. Those require humans, institutions, and equipment.

Computing requirements summary

Step	Tool	Minimum hardware	Cost
DNA sequencing	Commercial lab	None (send tissue)	~$3,000
Research planning	ChatGPT / Claude	Laptop	$20/mo
Mutation calling	GATK MuTect2	16GB RAM, 8-core CPU	Free (cloud: ~$5)
Protein prediction	AlphaFold Server	Browser only	Free
Protein prediction (bulk)	AlphaFold 2 local	8GB+ VRAM GPU, 64GB RAM	Free (cloud: ~$10)
Neoantigen ranking	pVACtools + NetMHCpan	16GB RAM, CPU	Free
mRNA synthesis	University lab	Not applicable	Collaboration

Total compute cost for the AI/bioinformatics steps: $50–100 in cloud credits if you don’t have a GPU locally. The $3,000 is almost entirely the sequencing.

What comes after AlphaFold: IsoDDE

If Paul Conyngham ran this pipeline today, he would have an even more powerful tool for Step 3.

In February 2026, Isomorphic Labs — Demis Hassabis’ AI drug design company, spun out of DeepMind — published their Drug Design Engine (IsoDDE). The numbers are significant:

2x+ the accuracy of AlphaFold 3 on novel protein-ligand structure prediction
19.8x better than AlphaFold 3 on antibody-antigen interface prediction
Outperforms gold-standard physics-based FEP methods on binding affinity prediction — in seconds instead of days
Identifies cryptic binding pockets — hidden drug targets that take labs six months to find manually

The cryptic pockets capability is the one that matters most for a pipeline like Conyngham’s. AlphaFold predicts the structure of a protein. IsoDDE goes further: it identifies the druggable surfaces on that structure — places where a drug molecule could bind and trigger an immune response. That is exactly what the neoantigen selection step is trying to do, and IsoDDE does it orders of magnitude faster and more accurately than any previous computational method.

IsoDDE is not yet publicly available as a free tool the way AlphaFold is — Isomorphic Labs is a pharmaceutical company and IsoDDE is their internal engine, though they have published the research. What it signals is that the Step 3 in Conyngham’s pipeline — which required custom ML expertise and AlphaFold — is rapidly becoming something a well-guided AI agent could handle with far less manual work.

The trajectory: AlphaFold (2020) made protein structure prediction accessible. AlphaFold 3 (2024) extended it to protein-ligand interactions. IsoDDE (2026) more than doubles that accuracy and adds binding affinity prediction. The next iteration will likely close the remaining gap between computational prediction and wet-lab validation.

Conyngham’s pipeline worked with 2024-era tools. With 2026-era tools, the same pipeline would be faster, cheaper, and more accurate at every step.

The honest scientific caveat

The Reddit skeptics are correct on one thing: this is n=1, no controls, unknown confounders. Rosie’s tumor may have responded for reasons unrelated to the vaccine. The approach — personalized mRNA neoantigen vaccines — is scientifically sound and exactly what Moderna and BioNTech are pursuing for human cancer. But a single dog is not clinical evidence.

What it is is a proof of concept that the full pipeline — from tumor sequencing to custom vaccine design — is now executable by a technically skilled individual with access to AI tools and a willing university collaborator, for the cost of a used car.

Dr. Thordarson put it best: “If we can do this for a dog, why aren’t we rolling this out to humans?”

The answer is regulatory, not scientific. The pipeline works. The bottleneck is the system around it.

🗣️ Community Discussion on Reddit

This post is generating active discussion in r/ChatGPT — including pushback from a genomics PhD and debate on regulatory pathways. Worth reading the thread:

The dog cancer vaccine pipeline is real — here is the exact 7-step pipeline to replicate his work
by u/the_ai_scientist in r/ChatGPT

Sources: Daily Mail · Awesome Agents · India Today