Rong2020 - Grover-clintox: A classification model to predict the likelihood of failure in clinical trials due to toxicity

ABSTRACT:

This model has been trained using the GROVER transformer and the Molecule Net dataset ClinTox, the authors trained a classification model to predict the likelihood of failure in clinical trials due to toxicity. The dataset has been built using FDA approved drugs (non-toxic) and a set of drugs that have failed at advanced clinical trial stages.

Model Type: Predicitive machine learning model.
Model Relevance: Probability that a molecule is approved by the FDA and probability that a molecule shows toxicity in clinical trials.
Model Encoded by: Amna Ali (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam

Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos6fza

SUBMITTER: Zainab Ashimiyu-Abdusalam 

PROVIDER: MODEL2406050004 | biostudies-other |

SECONDARY ACCESSION(S): 10.48550/arXiv.2007.02835

REPOSITORIES: biostudies-other