Innovative Drug-like Molecule Generation from
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
- University of Pittsburgh,Carnegie Mellon University (CMU)
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 12 November 2022
- Authors
- Haotian Zhang, Linxiaoyi Wan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
- Base model
- GraphBP
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
- 2,000,000 tokens
Total data points = 50,000 binding structures Previous estimate: 20 atoms/ligand = 1,000,000 data points [Final estimate: 1.0e6]
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Innovative Drug-like Molecule Generation from Flow-based Generative Model
- Last updated
- 28 November 2025
What the numbers mean
About this model
Innovative Drug-like Molecule Generation from was published by University of Pittsburgh,Carnegie Mellon University (CMU), in the country recorded as United States of America, during November 2022. The publishing organisation is categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of drug discovery.
Its starting point was an existing base model, GraphBP. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training consumed a corpus of around 2,000,000 tokens of text.
Answers
Innovative Drug-like Molecule Generation from — common questions
Innovative Drug-like Molecule Generation from— 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.
Innovative Drug-like Molecule Generation from— who created it?
It was published by University of Pittsburgh,Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia,Academia.
Innovative Drug-like Molecule Generation from— when was it released?
It was published in November 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Innovative Drug-like Molecule Generation from— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Innovative Drug-like Molecule Generation from— 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.
Innovative Drug-like Molecule Generation from— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.