Models

Dataset Information

0

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

Json Xml

Dataset's files

Source:
Action DRS
MODEL2404220001?filename=BioModelsMetadata%20-%20eos9tyg.csv Csv
Items per page:
1 - 1 of 1
altmetric image

Publications

Highly predictive and interpretable models for PAMPA permeability.

Sun Hongmao H   Nguyen Kimloan K   Kerns Edward E   Yan Zhengyin Z   Yu Kyeong Ri KR   Shah Pranav P   Jadhav Ajit A   Xu Xin X  

Bioorganic & medicinal chemistry 20161231 3

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds  ...[more]

Similar Datasets

Models Williams2022 - Permeable compound selection using in vitro ADME data (PAMPA pH 5).
2024-04-22 | MODEL2404220004 | BioModels
Models Menichetti2019 - Drug−Membrane Permeability across Chemical Space
2024-08-07 | MODEL2408070001 | BioModels
Models Chi2019 - In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach
2024-06-03 | MODEL2406030003 | BioModels
Models Kim2022 - BayeshERG: A deep learning model for predicting hERG channel blockers
2024-08-06 | MODEL2408060001 | BioModels
Models Wong2024 - Discovery of a structural class of antibiotics with explainable deep learning
2024-05-08 | MODEL2405080002 | BioModels
Models Wong2024 - Cytotoxicity Prediction from Human Cell Lines
2024-05-13 | MODEL2405130002 | BioModels
Transcriptomics CXCL10 promotes vascular permeability upon TNF-induced release from pericytes
2022-07-01 | GSE203505 | GEO
Genomics Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [ChIP-Seq]
2014-01-01 | E-GEOD-43786 | biostudies-arrayexpress
Transcriptomics Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [RNA-Seq]
2014-01-01 | E-GEOD-43788 | biostudies-arrayexpress
Models Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning
2024-05-08 | MODEL2405080003 | BioModels