readme: ---
  tags: 
  - chemistry
  ---
  
  # ChemGPT 19M
  ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
  
  ## Model description
  ChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.
  
  ## Intended uses & limitations
  
  ### How to use
  You can use this model directly from the 🤗/transformers library.
  
  ### Limitations and bias
  This model was trained on a subset of molecules from PubChem. You can use this model to generate molecules, but it is mostly intended to be used for investigations of the effects of pre-training and fine-tuning on downstream datasets.
  
  ## Training data
  PubChem10M, a dataset of SMILES strings from PubChem, available via [DeepChem](https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/pubchem_10m.txt.zip).
  
  ## Training procedure
  
  ### Preprocessing
  SMILES strings were converted to SELFIES using version 1.0.4 of the SELFIES library.
  
  
  ### Pretraining
  See code in the [LitMatter repository](https://github.com/ncfrey/litmatter/blob/main/lit_models/lit_chemgpt.py).
  
  ### BibTeX entry and citation info
  ```
  @article{frey_soklaski_axelrod_samsi_gomez-bombarelli_coley_gadepally_2022, 
  place={Cambridge}, title={Neural Scaling of Deep Chemical Models}, 
  DOI={10.26434/chemrxiv-2022-3s512}, journal={ChemRxiv}, publisher={Cambridge Open Engage}, 
  author={Frey, Nathan and Soklaski, Ryan and Axelrod, Simon and Samsi, Siddharth and Gomez-Bombarelli, Rafael and Coley, Connor and Gadepally, Vijay}, 
  year={2022}} This content is a preprint and has not been peer-reviewed.
  ```
  
  ```
  Frey, Nathan, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gomez-Bombarelli, Connor Coley, and Vijay Gadepally. 
  "Neural Scaling of Deep Chemical Models." ChemRxiv (2022). Print. This content is a preprint and has not been peer-reviewed.
  ```