ExSelfRL
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
- Soochow University
- Organisation type
- Academia
- Country
- Taiwan
- Published
- 20 September 2024
- Authors
- Jing Wang, Fei Zhu
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
they randomly select 5e5 molecules for training
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
- ExSelfRL: An exploration-inspired self-supervised reinforcement learning approach to molecular generation
- Last updated
- 28 November 2025
What the numbers mean
Background
ExSelfRL was published by Soochow University, in Taiwan, in September 2024. It comes out of academia.
It works in Biology, and is recorded as doing drug discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
ExSelfRL — common questions
What is ExSelfRL used for?
ExSelfRL works in Biology, and is recorded as handling 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.
What GPU do I need to run ExSelfRL?
None. ExSelfRL 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.
Is ExSelfRL open source?
The licensing for ExSelfRL was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ExSelfRL have?
No parameter count has been published for ExSelfRL, which is why no memory or speed figure appears on this page.
Who created ExSelfRL?
ExSelfRL was published by Soochow University, based in Taiwan, categorised as academia.
When was ExSelfRL released?
ExSelfRL 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.
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.