PepINVENT
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- AstraZeneca,Chalmers University of Technology
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, Sweden
- Published
- 21 September 2024
- Authors
- Gökçe Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He, Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola Engkvist, Leonardo De Maria
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein generation, Protein design
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
- Epochs
- 24
1,000,000 peptides * 0.9 training split = 900,000 training peptides 900,000 peptides * 12 tokens per peptide = 10,800,000 tokens Final estimate: 10,800,000 tokens (1.08e7)
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- How it was established
- Hardware
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
Apache 2.0 https://github.com/MolecularAI/PepINVENT
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 17
Sources
Where this record came from and when it was last checked.
- Reference
- PepINVENT: Generative peptide design beyond the natural amino acids
- Last updated
- 25 May 2026
What the numbers mean
About this model
PepINVENT was published by AstraZeneca,Chalmers University of Technology, in United Kingdom of Great Britain and Northern Ireland, in September 2024. The organisation is categorised as industry,Academia.
It works in Biology, and is recorded as doing protein generation, Protein design.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
PepINVENT — common questions
How many parameters does PepINVENT have?
No parameter count has been published for PepINVENT, which is why no memory or speed figure appears on this page.
Who created PepINVENT?
PepINVENT was published by AstraZeneca,Chalmers University of Technology, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia.
When was PepINVENT released?
PepINVENT 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 PepINVENT used for?
PepINVENT works in Biology, and is recorded as handling protein generation, Protein design. 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.
Where can I download PepINVENT?
The weights for PepINVENT are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run PepINVENT?
We cannot say. PepINVENT has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is PepINVENT open source?
Its weights are published, so PepINVENT can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.