Sun2017 - predictive and interpretable models for PAMPA permeability 7

ABSTRACT: Parallel Artificial Membrane Permeability is an in vitro surrogate to determine the permeability of drugs across cellular membranes. PAMPA at pH 7.4 was experimentally determined in a dataset of 5,473 unique compounds by the NIH-NCATS. 50% of the dataset was used to train a classifier (SVM) to predict the permeability of new compounds, and validated on the remaining 50% of the data, rendering an AUC = 0.88. The Peff was converted to logarithmic, log Peff value lower than 2.0 were considered to have low to moderate permeability, and those with a value higher than 2.5 were considered as high-permeability compounds. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as highly or low/moderate permeable. Model Encoded by: Pauline (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos9tyg

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2404220001 | BioModels | 2024-04-23

REPOSITORIES: BioModels