MMAPLE
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
- City University of New York,Cornell University
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 9 September 2024
- Authors
- You Wu, Li Xie, Yang Liu, Lei Xie
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein-ligand binding affinity prediction
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
Experiment 3: "After removing duplicates and unusable data, the dataset contained a total of 1,667,708 sam- ples including 357,213 unique compounds and 168,517 unique proteins." Binary classification with 1 prediction target per sample.
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
The code to reproduce results, together with documentation, is available on GitHub at https://github.com/XieResearchGroup/MMAPLE) CC BY 4.0
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
- Semi-supervised meta-learning elucidates understudied molecular interactions
- Last updated
- 28 November 2025
What the numbers mean
About this model
MMAPLE was published by City University of New York,Cornell University, in United States of America, in September 2024. academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein-ligand binding affinity prediction.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
MMAPLE — common questions
Is MMAPLE open source?
Its weights are published, so MMAPLE 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.
How many parameters does MMAPLE have?
No parameter count has been published for MMAPLE, which is why no memory or speed figure appears on this page.
Who created MMAPLE?
MMAPLE was published by City University of New York,Cornell University, based in United States of America, categorised as academia,Academia.
When was MMAPLE released?
MMAPLE 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 MMAPLE used for?
MMAPLE works in Biology, and is recorded as handling protein-ligand binding affinity prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download MMAPLE?
The weights for MMAPLE 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 MMAPLE?
We cannot say. MMAPLE 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.
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.