Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- ETH Zurich,University of Zurich,ETH AI Center
- Organisation type
- Academia,Academia,Research collective
- Country
- Switzerland
- Published
- 19 August 2024
- Authors
- Heath Arthur-Loui, Amina Mollaysa, Michael Krauthammer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- 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.
- Training data
- tokens
- Epochs
- 500
QM9 Dataset: 113,885 molecules × 40 tokens = 4,555,400 tokens ZINC250k Dataset: 250,000 molecules × 120 tokens = 30,000,000 tokens Total: 4,555,400 + 30,000,000 = 34,555,400 tokens ≈ 3.5 × 10^7 tokens
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.
- How it was established
- Hardware
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 4090
- Chips used
- 1
- Power draw
- 487 W
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
- Open — downloadable
- Model access
- Open weights (non-commercial)
- Training code
- Open (non-commercial)
no clear license https://github.com/HeathArhturLouis/Rethinking-Molecular-Design-Integrating-Latent-Variable-and-Autoregressive-Models-for-Enhanced-Goal
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
- Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
- Last updated
- 28 November 2025
What the numbers mean
Background
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation was published by ETH Zurich,University of Zurich,ETH AI Center, in the country recorded as Switzerland, during August 2024. The category the publisher falls under is academia,Academia,Research collective.
It works in the domain of Biology, and is recorded as performing the task of drug discovery.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation — common questions
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— 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.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— who created it?
It was published by ETH Zurich,University of Zurich,ETH AI Center, based in Switzerland, an organisation categorised as academia,Academia,Research collective.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— when was it released?
It was published in August 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.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— what is it used for?
It works in the domain of Biology, 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.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.