LEP-AD
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
- King Abdullah University of Science and Technology (KAUST),Karolinska Institute
- Organisation type
- Academia,Academia
- Country
- Saudi Arabia, Sweden
- Published
- 15 March 2023
- Authors
- Anuj Daga, Sumeer Ahmad Khan, David Gomez Cabrero, Robert Hoehndorf, Narsis A. Kiani, Jesper Tegner
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein interaction prediction, Drug discovery, Protein-ligand binding affinity prediction
- Base model
- ESM2-3B
- Numerical format
- FP16
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.
- Parameters
- 3B
- Training data
- 1,244,420 tokens
Uses ESM-2 3B. Table 2 gives details on the non-ESM layers. The GCN appears to have about 3.31M parameters and the linear layers should have 771k and 3.3M, respectively. So total is ~3.007B
Largest dataset appears to be STITCH, at 1244420 drug-target pairs.
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
https://github.com/adaga06/LEP-AD unclear license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 1
Tables 3-5 "We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold)"
Sources
Where this record came from and when it was last checked.
- Reference
- LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts Drug Target Interactions
- Last updated
- 28 November 2025
What the numbers mean
What this model is
LEP-AD was published by King Abdullah University of Science and Technology (KAUST),Karolinska Institute, in the country recorded as Saudi Arabia, during March 2023. The publishing organisation is categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein interaction prediction, Drug discovery, Protein-ligand binding affinity prediction.
Rather than being trained from scratch, it is derived from ESM2-3B. Most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training consumed a corpus of around 1,244,420 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
LEP-AD — common questions
LEP-AD— how many parameters does it have?
It has a parameter count of 3B. Uses ESM-2 3B. Table 2 gives details on the non-ESM layers. The GCN appears to have about 3.31M parameters and the linear layers should have 771k and 3.3M, respectively. So total is ~3.007B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
LEP-AD— who created it?
It was published by King Abdullah University of Science and Technology (KAUST),Karolinska Institute, based in Saudi Arabia, an organisation categorised as academia,Academia.
LEP-AD— when was it released?
It was published in March 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
LEP-AD— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein interaction prediction, Drug discovery, Protein-ligand binding affinity prediction. 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.
LEP-AD— 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.
LEP-AD— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.