DTI-LM
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
- University of Central Florida
- Organisation type
- Academia
- Country
- United States of America
- Published
- 2 September 2024
- Authors
- Khandakar Tanvir Ahmed, Md. Istiaq Ansari, Wei Zhang
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
- 6,041 tokens
DrugBank: 6,041 BindingDB: 4,040 Yamanishi_08: 3,448 Luo's Dataset: 1,526 Total = 6,041 + 4,040 + 3,448 + 1,526 = 15,055 datapoints
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
- DTI-LM: language model powered drug–target interaction prediction
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DTI-LM was published by University of Central Florida, in United States of America, in September 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein-ligand binding affinity prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
It was trained on about 6,041 tokens of text.
Answers
DTI-LM — common questions
When was DTI-LM released?
DTI-LM 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 DTI-LM used for?
DTI-LM works in Biology, and is recorded as handling 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.
What GPU do I need to run DTI-LM?
None. DTI-LM 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 DTI-LM open source?
The licensing for DTI-LM 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 DTI-LM have?
No parameter count has been published for DTI-LM, which is why no memory or speed figure appears on this page.
Who created DTI-LM?
DTI-LM was published by University of Central Florida, based in United States of America, categorised as academia.
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.