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--- |
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datasets: |
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- sagawa/ZINC-canonicalized |
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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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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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This model was trained from scratch on the ZINC dataset converted to SAFE format for molecule generation tasks. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3887 |
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## Model description |
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SAFE_100M is a transformer-based model designed for molecular generation tasks. It was trained on the ZINC dataset (https://huggingface.co./datasets/sagawa/ZINC-canonicalized), which has been converted to the SAFE (SMILES Augmented For Encoding) format. This format is specifically tailored for improved molecular representation in machine learning tasks. |
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The model is intended to generate valid and diverse molecular structures, which can be useful in various applications such as drug discovery, materials science, and chemical engineering. |
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This model utilizes the SAFE framework, which was introduced in the following paper: |
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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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We acknowledge and thank the authors for their valuable contribution to the field of molecular design. |
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## Intended uses & limitations |
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This model is primarily intended for: |
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- Generating molecular structures |
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- Exploring chemical space for drug discovery |
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- Assisting in the design of new materials |
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Limitations: |
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- The model's output should be validated by domain experts before practical application |
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- Generated molecules may not always be synthetically feasible |
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- The model's knowledge is limited 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), which was converted to the SAFE format. The ZINC dataset is a large collection of commercially available chemical compounds for virtual screening. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 100 |
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- eval_batch_size: 100 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 200 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10000 |
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- training_steps: 250000 |
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### Framework versions |
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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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