Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Menichetti2019 - Drug−Membrane Permeability across Chemical Space

ABSTRACT: Using Coarse Grained (CG) models, where several atoms are aggregated into a single bead, the authors obtain a set of 500,000 compounds with their simulated permeability across a single-component DOPC lipid bilayer. With this approach, the authors are able to cover a large and representative portion of the chemical space. We have used the data generated in this publication to train a simple regression model to predict compound permeability. Model Type: Predictive machine learning model. Model Relevance: Predicition of Passive permeability based on simulations 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/eos2hbd

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2408070001 | BioModels | 2024-08-07

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2408070001?filename=BioModelsMetadata%20-%20eos2hbd.csv	Csv

Items per page:

1 - 1 of 1

Publications

Drug-Membrane Permeability across Chemical Space.

Menichetti Roberto R Kanekal Kiran H KH Bereau Tristan T

ACS central science 20190108 2

Unraveling the relation between the chemical structure of small druglike compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure-permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous-bu ...[more]

PMID: 30834317

Similar Datasets

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

Project description: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

2024-06-03 | MODEL2406030003 | BioModels

Merget2013 - Mycobacterium tuberculosis permeability prediction tool

Project description:MycPermCheck predicts potential to permeate the Mycobacterium tuberculosis cell membrane based on physicochemical properties. Due to the lack of reliable experimental datapoints, the authors defined the training set using molecules that are active against M.tb. Model Type: Predictive machine learning model. Model Relevance: Probability of permeability across the M.tb cell wall. 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/eos8d8a

2024-05-21 | MODEL2405210004 | BioModels

Jiménez-Luna2021 - Coloring molecules for Caco-2 cell permeability

Project description:By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks such as pCaco-2 cell permeability as the case for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions. This model has been trained using experimental data on the permeability of molecules across Caco2 cell membranes (Papp, cm s-1). Model Type: Predictive machine learning model. Model Relevance: Predicts Log10 of the Passive permeability in cm s-1. 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/eos1af5

2024-05-21 | MODEL2405210006 | BioModels

Drug-Membrane Permeability across Chemical Space.

Project description:Unraveling the relation between the chemical structure of small druglike compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure-permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous-but smoothed out-structure-property relationship problem. This is achieved by relying on a physics-based coarse-grained model that reduces the size of chemical space, enabling a comprehensive exploration of this space with greatly reduced computational cost. We perform high-throughput coarse-grained (HTCG) simulations to derive a permeability surface in terms of two simple molecular descriptors-bulk partitioning free energy and pK a. The surface is constructed by exhaustively simulating all coarse-grained compounds that are representative of small organic molecules (ranging from 30 to 160 Da) in a high-throughput scheme. We provide results for acidic, basic, and zwitterionic compounds. Connecting back to the atomic resolution, the HTCG predictions for more than 500?000 compounds allow us to establish a clear connection between specific chemical groups and the resulting permeability coefficient, enabling for the first time an inverse design procedure. Our results have profound implications for drug synthesis: the predominance of commonly employed chemical moieties narrows down the range of permeabilities.

| S-EPMC6396385 | biostudies-literature

Sun2017 - predictive and interpretable models for PAMPA permeability 7

Project description: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

2024-04-23 | MODEL2404220001 | BioModels

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [ChIP-Seq]

Project description:Vascular permeability is frequently associated with inflammation and it is triggered by chemokines and by a cohort of secreted permeability factors, such as VEGF. In contrast, here we showed that the physiological vascular permeability that precedes implantation is directly controlled by progesterone receptor (PR) and it is independent of VEGF. Both global and endothelial-specific deletion of PR block physiological vascular permeability in the uterus while misexpression of PR in the endothelium of other organs results in ectopic vascular leakage. Integration of genome-wide transcriptional profile of endothelium and ChIP-sequencing revealed that PR induces a NR4A1 (Nur77/TR3) specific transcriptional program that broadly regulates vascular permeability in response to progesterone. This program triggers concurrent suppression of several junctional proteins and leads to an effective, timely and venule-specific regulation of vascular barrier function. Silencing NR4A1 blocks PR-mediated permeability responses indicating a direct link between PR and NR4A1. These results reveal a previously unknown function for progesterone receptor on endothelial cell biology with consequences to physiological vascular permeability and implications to the clinical use of progestins and anti-progestins on blood vessel integrity. Examination of PR binding sites in HUVEC cells using ChIP-seq (non-infected-negative control, PR infected followed by ligand treatment-PR+P or vehicle PR)

2014-01-01 | E-GEOD-43786 | biostudies-arrayexpress

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [RNA-Seq]

Project description:Vascular permeability is frequently associated with inflammation and it is triggered by chemokines and by a cohort of secreted permeability factors, such as VEGF. In contrast, here we showed that the physiological vascular permeability that precedes implantation is directly controlled by progesterone receptor (PR) and it is independent of VEGF. Both global and endothelial-specific deletion of PR block physiological vascular permeability in the uterus while misexpression of PR in the endothelium of other organs results in ectopic vascular leakage. Integration of genome-wide transcriptional profile of endothelium and ChIP-sequencing revealed that PR induces a NR4A1 (Nur77/TR3) specific transcriptional program that broadly regulates vascular permeability in response to progesterone. This program triggers concurrent suppression of several junctional proteins and leads to an effective, timely and venule-specific regulation of vascular barrier function. Silencing NR4A1 blocks PR-mediated permeability responses indicating a direct link between PR and NR4A1. These results reveal a previously unknown function for progesterone receptor on endothelial cell biology with consequences to physiological vascular permeability and implications to the clinical use of progestins and anti-progestins on blood vessel integrity. Examination of PR target genes in human umbilical vein endothelial cells (HUVECs) using RNA-seq (PR infected only -PR only and PR infected followed by ligand treatment-PR+P)

2014-01-01 | E-GEOD-43788 | biostudies-arrayexpress

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [ChIP-Seq]

2014-01-01 | GSE43786 | GEO

Physiological Vascular Permeability Requires Induction of Endothelial NR4A1 by Progesterone Receptor [RNA-Seq]

2014-01-01 | GSE43788 | GEO

Alcohol feeding promotes colonic permeability and changes in colonic organoid stem cell fate

Project description:Alcohol increases intestinal permeability to pro-inflammatory microbial products including endotoxin and this is a key step in alcohol-induced organ damage such as alcoholic liver disease. Elevated intestinal permeability is observed even after a period of sobriety. We sought to investigate mechanisms contributing to long-term effects of alcohol on intestinal permeability by testing the hypothesis that alcohol affects intestinal stem cells using ex vivo organoids generated from jejunum and colon from chronic alcohol fed mice.

2018-02-14 | GSE99291 | GEO