Pu2019 - eToxPred: an ML-based approach to estimate the toxicity, and synthetic accessibility of drug candidates

The eToxPred tool has been developed to predict, on one hand, the synthetic accessibility (SA) score, or how easy it is to make the molecule in the laboratory, and, on the other hand, the toxicity (Tox) score, or the probability of the molecule of being toxic to humans. The authors trained and cross-validated both predictors on a large number of datasets, and demonstrated the method usefulness in building virtual custom libraries.

Model Type: Predicitive machine learning model.
Model Relevance: Predicts Synthetic Accesibility and Toxicity score of a chemical compound
Model Encoded by: Miquel Duran-Frigola (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam

Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos92sw