TxGNN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Harvard Medical School,Harvard-MIT Program in Health Sciences and Technology,The Mount Sinai Hospital (New York),Broad Institute,Harvard Data Science Initiative,Harvard University,Stanford University
- Organisation type
- Academia,Research collective,Research collective,Academia,Academia,Academia
- Country
- United States of America
- Published
- 25 September 2024
- Authors
- Kexin Huang, Payal Chandak, Qianwen Wang, Shreyas Havaldar, Akhil Vaid, Jure Leskovec, Girish N. Nadkarni, Benjamin S. Glicksberg, Nils Gehlenborg, Marinka Zitnik
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"The KG is heterogeneous, with 10 types of nodes and 29 types of undi- rected edges. It contains 123,527 nodes and 8,063,026 edges." Prediction targets are on the level of node pairs with limited labeled examples.
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Hosted access (no API)
- Training code
- Open source
Python implementation of the methodology developed and used in the present study is available via the project website at https://zitniklab.hms.harvard.edu/projects/TxGNN. The code to reproduce results, documentation and usage examples is at https://github.com/mims-harvard/TxGNN [MIT license]. We developed a web-based app available at http://txgnn.org to access TxGNN’s predictions and predictive rationales. [I don't see model checkpoints in the repo] here is an interface to access the model: ht…
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- A foundation model for clinician-centered drug repurposing
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
TxGNN was published by Harvard Medical School,Harvard-MIT Program in Health Sciences and Technology,The Mount Sinai Hospital (New York),Broad Institute,Harvard Data Science Initiative,Harvard University,Stanford University, in the country recorded as United States of America, during September 2024. It comes out of an organisation categorised as academia,Research collective,Research collective,Academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of drug discovery.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
TxGNN — common questions
TxGNN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
TxGNN— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
TxGNN— who created it?
It was published by Harvard Medical School,Harvard-MIT Program in Health Sciences and Technology,The Mount Sinai Hospital (New York),Broad Institute,Harvard Data Science Initiative,Harvard University,Stanford University, based in United States of America, an organisation categorised as academia,Research collective,Research collective,Academia,Academia,Academia.
TxGNN— when was it released?
It was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
TxGNN— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of drug discovery. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
TxGNN— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.