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Chi2019 - In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

ABSTRACT: The authors provide a dataset of 200 small molecules and their experimentally measured permeability in a PAMPA assay. Using this data, we have trained a model that predicts the logarithm of the effective permeability coefficient. Model Type: Predictive machine learning model. Model Relevance: Predicts pampa-permeability. Model Encoded by: Miquel Duran Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos97yu

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2406030003 | BioModels | 2024-06-03

REPOSITORIES: BioModels

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Publications

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach.

Chi Cheng-Ting CT   Lee Ming-Han MH   Weng Ching-Feng CF   Leong Max K MK  

International journal of molecular sciences 20190628 13

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative str  ...[more]