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KC2021 - A machine learning platform to estimate anti-SARS-CoV-2 activities


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

Dataset's files

Source:
Action DRS
MODEL2405130004?filename=BioModelsMetadata%20-%20eos8fth.csv Csv
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