Biotech Research Studio puts the entire published research landscape for a disease at your fingertips. Papers, clinical trials, drug targets, protein structures. AI reads it all. You ask the questions. Configure it for any disease.
Thousands of papers are published every year on every disease. Clinical trials open and close. New drug targets are identified. But this information is scattered across databases, written in specialist language, and hard to navigate without years of training.
PubMed has millions of papers. For any given disease, hundreds are relevant. Nobody has time to read them all, let alone connect the dots across them.
Research papers are written for specialists. The terminology, methodology, and implications are opaque to anyone outside the field.
Papers are on PubMed. Trials are on ClinicalTrials.gov. Protein structures are on AlphaFold. Drug data is elsewhere. Nothing is connected.
Biotech Research Studio connects the dots for you. It pulls papers, reads them with AI, extracts the key findings, and puts everything in one place. You get the knowledge without the years of training.
Full text ingested and analyzed. Not abstracts. See results →
Extracted by Gemma with target, mechanism, and evidence level. See candidates →
Mapped to AlphaFold 3D structures you can inspect. View targets →
Structured data you can query, filter, and build on. View raw data →
Drug candidates, targets, mechanisms. Structured JSON.
Knowledge base grows overnight. Wiki compiles itself.
Ask questions, get cited answers. 22 languages.
Real output from Experiment #4. Gemma 4 26B read 63,979 characters of a paper and returned this in under 5 minutes.
Gemma 4 did this 75 times across 75 papers. 4,976,515 characters total. Found 42 drug candidates across 5 validated protein targets. On a Mac M2. Cost: $0.
See full results arrow_forward| Target | Confidence | Druggability | Top drug candidate | Evidence |
|---|---|---|---|---|
| MSH3 |
90
|
HIGH | di-siRNA (78% expansion reduction) | animal model |
| PMS1 |
85
|
HIGH | branaplam (splice modulator) | cell model |
| FAN1 |
80
|
MEDIUM | miR-124-3p antagomir | cell model |
| MLH1 |
70
|
MEDIUM | cyclic peptide inhibitors | preclinical |
| LIG1 |
60
|
EMERGING | ligase fidelity enhancers | concept |
The full pipeline runs on Kaggle's free T4 GPU. Pick a disease, point it at PubMed, watch Gemma 4 extract drug candidates from real papers.
Open Kaggle Notebook open_in_newDefine your disease, targets, and search queries in one file. The studio builds everything else: pipelines, agents, website, chatbot.
Huntington's Disease Studio
LRRK2, GBA, SNCA
SOD1, TDP-43, FUS
You know Python and ML. This gives you the biomedical data layer. Fork, configure, run experiments.
Automated literature review. The platform reads papers overnight. You focus on the science.
Research in plain language. 22 languages. A chatbot that cites its sources, not hallucinate.
Deploy a research workspace for your disease community. Open source. No vendor dependency.