DeepSA
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
- ShanghaiTech University
- Organisation type
- Academia
- Country
- China
- Published
- 2 November 2023
- Authors
- Shihang Wang, Lin Wang, Fenglei Li, Fang Bai
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Synthetic accessibility model
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
- tokens
- Epochs
- 20
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
- 1 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA GeForce RTX 3090 (1.60 × 10¹⁴ FLOP/s) 2. Training duration: Estimated based on dataset size and batch processing - 71,861,060 total samples (3,593,053 samples × 20 epochs) - 561,719 batches (batch size 128) - ~15.6 hours (56,172 seconds) 3. Utilization rate: 40% 4. Final calculation: 1.60 × 10¹⁴ FLOP/s × 1 GPU × 5.6 × 10⁴ s × 0.4 = 3.6 × 10¹⁸ FLOPs (Rounded to 1.0 × 10¹⁹ FLOPs)
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 3090
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
- Hosted access (no API)
- Training code
- Open (non-commercial)
https://github.com/Shihang-Wang-58/DeepSA
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- DeepSA: a deep-learning driven predictor of compound synthesis accessibility
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DeepSA was published by ShanghaiTech University, in the country recorded as China, during November 2023. It comes out of an organisation categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of synthetic accessibility model.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 1 × 10¹⁹ FLOP, on hardware recorded as NVIDIA GeForce RTX 3090. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
DeepSA — common questions
DeepSA— when was it released?
It was published in November 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.
DeepSA— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of synthetic accessibility model. These are the areas it was designed around; they describe intent rather than a hard boundary.
DeepSA— how much compute was used to train it?
Training consumed around 1 × 10¹⁹ FLOP, on hardware recorded as NVIDIA GeForce RTX 3090. 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.
DeepSA— 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.
DeepSA— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DeepSA— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
DeepSA— who created it?
It was published by ShanghaiTech University, based in China, an organisation 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.