KC2021 - A machine learning platform to estimate anti-SARS-CoV-2 activities
Ontology highlight
ABSTRACT: Predictor of several endpoints related to Sars-CoV-2. It provides predictions for Live Virus Infectivity, Viral Entry, Viral Replication, In Vitro Infectivity and Human Cell Toxicity using a combination of three models. Consensus results are obtained by averaging the prediction for the three different models for each activity and toxicity models. The models have been built using NCATS COVID19 data.
Model Type: Predictive machine learning model.
Model Relevance: Predicts activity of compound in COVID assay.
Model Encoded by: Pradnya (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos8fth
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2405130004 | BioModels | 2024-05-13
REPOSITORIES: BioModels
ACCESS DATA