{"database":"biostudies-other","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Zainab Ashimiyu-Abdusalam"],"journal":["Advances in Neural Information Processing Systems 33 (NeurIPS 2020)"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/MODEL2406050004"],"repository":["biostudies-other"],"additional_accession":["10.48550/arXiv.2007.02835"],"pubmed_authors":["Amna Ali","Zainab Ashimiyu-Abdusalam"]},"is_claimable":false,"name":"Rong2020 - Grover-clintox: A classification model to predict the likelihood of failure in clinical trials due to toxicity","description":"<p>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. </p><p><normal>Model Type:</normal> Predicitive machine learning model.<br><normal>Model Relevance:</normal> Probability that a molecule is approved by the FDA and probability that a molecule shows toxicity in clinical trials.<br><normal>Model Encoded by:</normal>  Amna Ali (Ersilia)<br><normal>Metadata Submitted in BioModels by:</normal> Zainab Ashimiyu-Abdusalam</p><p>Implementation of this model code by <a href=\"https://ersilia.io/\">Ersilia</a> is available here: <br><a href=\"https://github.com/ersilia-os/eos6fza\">https://github.com/ersilia-os/eos6fza</a></p><img src=\"https://www.ebi.ac.uk/biomodels/static-assets/images/ersilia-logo.png\" alt=\"Ersilia Logo\" width=\"150\">","dates":{"release":"2024-06-05T00:00:00Z","modification":"2025-07-14T17:01:33.709Z","creation":"2025-03-31T13:26:41.778Z"},"accession":"MODEL2406050004","cross_references":{"bao":["0700004"],"stato":["STATO:0000031"],"ncit":["NCIT:C103240","NCIT:C41340","NCIT:C16309","NCIT:C71104","C154407","NCIT:C39536"],"edam":["topic_3474"],"cheminf":["CHEMINF:000018"],"doi":["10.48550/arXiv.2007.02835"],"unknown":["datasets-1"]}}