Shao2022 - S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules

The model uses Word2Vec, a natural language processing technique to represent SMILES strings. The model was trained on over 4000 small molecules with associated experimental HBV inhibition data (IC50) to classify compounds as HBV inhibitors or non-inhibitors.

Model Type: Predictive machine learning model.
Model Relevance: Predicts Probability of inhibition of HBV.
Model Encoded by: Emmanuel Onwuegbusi (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam

Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos8lok