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 the country recorded as United States of America, during October 1992. The category the publisher falls under is government.
It works in the domain of Medicine, and is recorded as performing the task of 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 arithmetic totalling around 5.3 × 10⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 141 tokens of text.
Its inclusion criterion: historical significance.
Answers
Cancer drug mechanism prediction — common questions
Cancer drug mechanism prediction— how many parameters does it have?
It has a parameter count of 0.6K. “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.
Cancer drug mechanism prediction— who created it?
It was published by National Cancer Institute, based in United States of America, an organisation categorised as government.
Cancer drug mechanism prediction— when was it released?
It 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.
Cancer drug mechanism prediction— what is it used for?
It works in the domain of Medicine, and is recorded as handling the task of drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Cancer drug mechanism prediction— how much compute was used to train it?
Training consumed 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.
Cancer drug mechanism prediction— 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.
Cancer drug mechanism prediction— 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?
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