| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| README.MD | 2025-11-30 | 792 Bytes | |
| Totals: 1 Item | 792 Bytes | 0 |
### Descriptor-free BiLSTM model for Endocrine Toxicity Prediction Hello! Welcome to the official repository of 'Explainable Bidirectional Long Short-Term Memory Networks Learn Chemistry from SMILES for Predicting Toxicity of Androgen and Estrogen Receptor Targeting Chemicals'! This project provides a descriptor-free deep learning framework based on a Bidirectional LSTM (BiLSTM) architecture to predict endocrine toxicity toward the androgen receptor (AR) and estrogen receptor (ER) directly from SMILES strings. The model performs binary classification (toxic / non-toxic) and integrates explainable artificial intelligence (XAI) through Captum, enabling token-level and substructure-level interpretation of SMILES. + not yet versioned + look in the code tab for the latest version
