DrugTar
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
- Isfahan University of Technology
- Organisation type
- Academia
- Country
- Iran (Islamic Republic of)
- Published
- 24 September 2024
- Authors
- Niloofar Borhani, Iman Izadi, View ORCID ProfileAli Motahharynia, Mahsa Sheikholeslami, Yousof Gheisari
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein property prediction, Drug discovery
- Base model
- ESM2-650M
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
- 650M
- Training data
- 4,068 tokens
- Epochs
- 15
4068 (ProTar-II dataset)*300(estimated tokens per protein)=1220400
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies
- Last updated
- 28 November 2025
What the numbers mean
Background
DrugTar was published by Isfahan University of Technology, in the country recorded as Iran (Islamic Republic of), during September 2024. The category the publisher falls under is academia.
It works in the domain of Biology, and is recorded as performing the task of protein property prediction, Drug discovery.
Rather than being trained from scratch, it is derived from ESM2-650M. That is the usual way a specialised model is produced.
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 4,068 tokens of text.
Answers
DrugTar — common questions
DrugTar— how many parameters does it have?
It has a parameter count of 650M. 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.
DrugTar— who created it?
It was published by Isfahan University of Technology, based in Iran (Islamic Republic of), an organisation categorised as academia.
DrugTar— when was it released?
It 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.
DrugTar— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein property prediction, Drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DrugTar— 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.
DrugTar— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.