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--- |
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library_name: transformers |
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tags: |
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- safe |
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- datamol-io |
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- molecule-design |
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- smiles |
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- generated_from_trainer |
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datasets: |
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- sagawa/ZINC-canonicalized |
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model-index: |
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- name: SAFE_100M |
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results: [] |
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--- |
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# SAFE_100M |
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SAFE_100M is a cutting-edge transformer-based model developed for molecular generation tasks. Trained from scratch on the [ZINC dataset](https://huggingface.co./datasets/sagawa/ZINC-canonicalized) converted to the SAFE (SMILES Augmented For Encoding) format, SAFE_100M achieves a loss of **0.3887** on its evaluation set, demonstrating robust performance in generating valid and diverse molecular structures. |
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## Model Description |
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SAFE_100M leverages the [SAFE framework](#references) to enhance molecular representation through the SMILES Augmented For Encoding format. By utilizing the comprehensive [ZINC dataset](https://huggingface.co./datasets/sagawa/ZINC-canonicalized), the model excels in navigating chemical space, making it highly effective for applications such as: |
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- **Drug Discovery** |
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- **Materials Science** |
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- **Chemical Engineering** |
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The transformer architecture ensures the generation of both valid and structurally diverse molecules, facilitating innovative solutions across various scientific disciplines. |
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## Intended Uses & Limitations |
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### Intended Uses |
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SAFE_100M is designed to support: |
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- **Molecular Structure Generation**: Creating novel molecules with desired properties. |
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- **Chemical Space Exploration**: Identifying potential candidates for drug development. |
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- **Material Design Assistance**: Innovating new materials with specific characteristics. |
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### Limitations |
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While SAFE_100M is a powerful tool, users should be aware of the following limitations: |
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- **Validation Requirement**: Outputs should be reviewed by domain experts before practical application. |
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- **Synthetic Feasibility**: Generated molecules may not always be synthesizable in a laboratory setting. |
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- **Dataset Boundaries**: The model's knowledge is confined to the chemical space represented in the ZINC dataset. |
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## Training and Evaluation Data |
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The model was trained on the [ZINC dataset](https://huggingface.co./datasets/sagawa/ZINC-canonicalized), a large repository of commercially available chemical compounds optimized for virtual screening. This dataset was transformed into the SAFE format to enhance molecular encoding for machine learning applications. |
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## Training Procedure |
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### Training Hyperparameters |
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SAFE_100M was trained with the following hyperparameters: |
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- **Learning Rate**: `0.0001` |
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- **Training Batch Size**: `100` |
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- **Evaluation Batch Size**: `100` |
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- **Random Seed**: `42` |
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- **Gradient Accumulation Steps**: `2` |
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- **Total Training Batch Size**: `200` |
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- **Optimizer**: Adam (`betas=(0.9, 0.999)`, `epsilon=1e-08`) |
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- **Learning Rate Scheduler**: Linear with `10,000` warmup steps |
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- **Total Training Steps**: `250,000` |
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### Framework Versions |
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The training process utilized the following software frameworks: |
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- **Transformers**: `4.44.2` |
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- **PyTorch**: `2.4.0+cu121` |
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- **Datasets**: `2.20.0` |
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- **Tokenizers**: `0.19.1` |
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## Acknowledgements |
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We extend our gratitude to the authors of the SAFE framework for their significant contributions to the field of molecular design. |
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## References |
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```bibtex |
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@article{noutahi2024gotta, |
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title={Gotta be SAFE: a new framework for molecular design}, |
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author={Noutahi, Emmanuel and Gabellini, Cristian and Craig, Michael and Lim, Jonathan SC and Tossou, Prudencio}, |
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journal={Digital Discovery}, |
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volume={3}, |
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number={4}, |
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pages={796--804}, |
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year={2024}, |
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publisher={Royal Society of Chemistry} |
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} |
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``` |
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