README.md

---
license: mit
tags:
- chemistry
- smiles
widget:
- text: "^"
  example_title: "Sample molecule | SMILES"
---

# Model Card for Model hogru/MolReactGen-GuacaMol-Molecules

<!-- Provide a quick summary of what the model is/does. -->

MolReactGen is a model that generates molecules in SMILES format (this model) and [reaction templates in SMARTS format](https://huggingface.co/hogru/MolReactGen-USPTO50K-Reaction-Templates).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

MolReactGen is based on the the GPT-2 transformer decoder architecture and has been trained on the [GuacaMol dataset](https://figshare.com/projects/GuacaMol/56639). More information can be found in these [introductory slides](https://github.com/hogru/MolReactGen/blob/main/presentations/Slides%20(A4%20size).pdf).

- **Developed by:** Stephan Holzgruber
- **Model type:** Transformer decoder
- **License:** MIT

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/hogru/MolReactGen
- **Presentation:** https://github.com/hogru/MolReactGen/blob/main/presentations/Slides%20(A4%20size).pdf
- **Poster:** https://github.com/hogru/MolReactGen/blob/main/presentations/Poster%20(A0%20size).pdf

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

The main use of this model is to pass the master's examination of the author ;-)

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

The model can be used in a Hugging Face text generation pipeline. For the intended use case a wrapper around the raw text generation pipeline is needed. This is the [`generate.py` from the repository](https://github.com/hogru/MolReactGen/blob/main/src/molreactgen/generate.py).
The model has a default `GenerationConfig()` (`generation_config.json`) which can be overwritten. Depending on the number of molecules to be generated (`num_return_sequences` in the `JSON` file) this might take a while. The generation code above shows a progress bar during generation.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model generates molecules that are similar to the GuacaMol training data, which itself is based on [ChEMBL](https://www.ebi.ac.uk/chembl/). Any checks of the molecules, e.g. chemical feasiblitly, must be adressed by the user of the model.

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[GuacaMol dataset](https://figshare.com/projects/GuacaMol/56639)

### Training Procedure 

The default Hugging Face `Trainer()` has been used, with an `EarlyStoppingCallback()`.

### Preprocessing

The training data was pre-processed with a `PreTrainedTokenizerFast()` trained on the training data with a character level pre-tokenizer and Unigram as the sub-word tokenization algorithm with a vocabulary size of 88. Other tokenizers can be configured.

### Training Hyperparameters

- **Batch size:** 64
- **Gradient accumulation steps:** 4
- **Mixed precision:** fp16, native amp
- **Learning rate:** 0.0025
- **Learning rate scheduler:** Cosine
- **Learning rate scheduler warmup:** 0.1
- **Optimizer:** AdamW with betas=(0.9,0.95) and epsilon=1e-08
- **Number of epochs:** 50

More configuration (options) can be found in the [`conf`](https://github.com/hogru/MolReactGen/tree/main/src/molreactgen/conf) directory of the repository.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Please see the slides / the poster mentioned above.

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Please see the slides / the poster mentioned above.

### Results

Please see the slides / the poster mentioned above.


## Technical Specifications

### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2

### Hardware

- Local PC running Ubuntu 22.04
- NVIDIA GEFORCE RTX 3080Ti (12GB)