Cancer drug mechanism prediction
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
- National Cancer Institute
- Organisation type
- Government
- Country
- United States of America
- Published
- 16 October 1992
- Authors
- John N. Weinstein, Kurt W. Kohn, Michael R. Grever, Vellarkad N. Viswanadhan, Lawrence V. Rubinstein, Anne P. Monks, Dominic A. Scudiero, Lester Welch, Antonis D. Koutsoukos, August J. Chiausa, Kenneth D. Paull
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Medicine
- Task
- Drug discovery
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
- 0.6K
- Training data
- 141 tokens
“The network shown has 60 input PEs, one for each cell line, and 6 output PEs“ “Neural networks with three to nine hidden layer PEs used” 9*60 + 6*9 = 594
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.
- Training compute
- 5.3 × 10⁷ FLOP
- How it was established
- Operation counting
2*594*3*15000=53460000=5.35e7 “The extent of training was 15,000 presentations“
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
- Historical significance
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Neural computing in cancer drug development: predicting mechanism of action
- Last updated
- 28 November 2025
What the numbers mean
About this model
Cancer drug mechanism prediction was published by National Cancer Institute, in United States of America, in October 1992. government is the category the publisher falls under.
It works in Medicine, and is recorded as doing drug discovery.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 5.3 × 10⁷ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
It was trained on about 141 tokens of text.
Its inclusion criterion is historical significance.
Answers
Cancer drug mechanism prediction — common questions
How many parameters does Cancer drug mechanism prediction have?
Cancer drug mechanism prediction has 0.6K parameters. “The network shown has 60 input PEs, one for each cell line, and 6 output PEs“ “Neural networks with three to nine hidden layer PEs used” 9*60 + 6*9 = 594. 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.
Who created Cancer drug mechanism prediction?
Cancer drug mechanism prediction was published by National Cancer Institute, based in United States of America, categorised as government.
When was Cancer drug mechanism prediction released?
Cancer drug mechanism prediction was published in October 1992. 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 Cancer drug mechanism prediction used for?
Cancer drug mechanism prediction works in Medicine, and is recorded as handling drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Cancer drug mechanism prediction?
Around 5.3 × 10⁷ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Cancer drug mechanism prediction?
None. Cancer drug mechanism prediction 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 Cancer drug mechanism prediction open source?
The licensing for Cancer drug mechanism prediction 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?
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