This model is fine-tuned on the BBBP (Blood-Brain Barrier Penetration) dataset and is designed to classify chemical compounds as either penetrating or non-penetrating the blood-brain barrier. The input to the model is in the SELFIES (Self-referencing Embedded Strings) molecular representation. The model uses the APE (Atom Pair Encoding) tokenizer for tokenizing the input, with the vocabulary stored in the same repository as the model under the file name tokenizer.json. The model is intended for sequence classification tasks and should be loaded with the AutoModelForSequenceClassification class.

Model Details

Model Description

This is a 🤗 transformers model fine-tuned on the BBBP dataset to classify chemical compounds based on their ability to penetrate the blood-brain barrier. The model takes as input SELFIES molecular representations and uses the APE Tokenizer for input tokenization. The tokenizer’s vocabulary is stored in tokenizer.json within the same repository as the model.

Developed by: Miguelangel Leon
Funded by: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI:10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
Model type: Sequence Classification
Language(s) (NLP): Not applicable (SELFIES molecular representation)
License: MIT
Finetuned from model: mikemayuare/SELFYAPE

Model Sources

Paper : Leon, M., Perezhohin, Y., Peres, F. et al. Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling. Sci Rep 14, 25016 (2024). https://doi.org/10.1038/s41598-024-76440-8

Uses

Direct Use

This model can be used directly for binary classification of chemical compounds in terms of blood-brain barrier penetration. It is specifically designed for the BBBP dataset, and the inputs must be formatted as SELFIES strings.

Downstream Use

This model can be fine-tuned for other classification tasks involving chemical compounds, especially where SELFIES molecular representations are used.

Out-of-Scope Use

This model is not designed for tasks outside of chemical compound classification or for tasks that require natural language processing (NLP).

Bias, Risks, and Limitations

As this model is fine-tuned on the BBBP dataset, it may not generalize well to compounds outside of the training distribution or to compounds for which blood-brain barrier penetration is ambiguous or poorly represented in the data. Additionally, it is not applicable for tasks unrelated to chemical data.

Recommendations

Users should carefully evaluate the model on their specific dataset and task to ensure it is appropriate. The model may not perform well on novel chemical spaces or datasets that diverge significantly from the BBBP dataset.

How to Get Started with the Model

To use the model for classification, it must be loaded with the AutoModelForSequenceClassification class from 🤗 transformers. The APE tokenizer is required to process the input data, which should be formatted as SELFIES.

You can load the APE tokenizer and the model with the following steps:

python

# Install the APETokenizer from the repository
# !git clone https://github.com/mikemayuare/apetokenizer
# Load the tokenizer
from src.apetokenizer.ape_tokenizer import APETokenizer

tokenizer = APETokenizer()
tokenizer.load_vocabulary("tokenizer.json")

# Load the model
from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("mikemayuare/SELFY-APE-BBBP")