PepNet
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
- Shandong University
- Organisation type
- Academia
- Country
- China
- Published
- 28 September 2024
- Authors
- Jiyun Han, Tongxin Kong, Juntao Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design, Protein or nucleotide language model (pLM/nLM)
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
"Finally, the AIP training, valid, and testing sets contain 2516, 629, and 1049 samples, while the AMP training, valid, and testing sets consist of 5340, 1336, and 1670 samples, respectively." Binary classification where each sequence provides on gradient target.
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
- PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model
- Last updated
- 28 November 2025
What the numbers mean
About this model
PepNet was published by Shandong University, in China, in September 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein design, Protein or nucleotide language model (pLM/nLM).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
PepNet — common questions
Who created PepNet?
PepNet was published by Shandong University, based in China, categorised as academia.
When was PepNet released?
PepNet 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.
What is PepNet used for?
PepNet works in Biology, and is recorded as handling protein design, Protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run PepNet?
None. PepNet 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 PepNet open source?
The licensing for PepNet 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 PepNet have?
No parameter count has been published for PepNet, which is why no memory or speed figure appears on this page.
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.