Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates
Por um escritor misterioso
Last updated 31 agosto 2024
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://pubs.acs.org/cms/10.1021/acs.jcim.0c00525/asset/images/medium/ci0c00525_0004.gif)
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://www.science.org/cms/10.1126/sciadv.adf0873/asset/c8a18ae1-6c68-4796-b638-43aae4262e96/assets/images/large/sciadv.adf0873-f1.jpg)
Accurate global machine learning force fields for molecules with hundreds of atoms
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41598-022-22992-6/MediaObjects/41598_2022_22992_Fig2_HTML.png)
The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://pub.mdpi-res.com/molecules/molecules-28-07532/article_deploy/html/images/molecules-28-07532-ag-550.jpg?1699628726)
Molecules, Free Full-Text
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://iiif.elifesciences.org/lax:79932%2Felife-79932-fig5-v2.tif/full/1500,/0/default.jpg)
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://www.mdpi.com/molecules/molecules-25-06006/article_deploy/html/images/molecules-25-06006-g001.png)
Molecules, Free Full-Text
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://pubs.acs.org/cms/10.1021/acs.jcim.0c00525/asset/images/large/ci0c00525_0006.jpeg)
Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates
Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://pubs.acs.org/cms/10.1021/ci2001583/asset/images/medium/ci-2011-001583_0006.gif)
P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs42003-022-03416-7/MediaObjects/42003_2022_3416_Fig1_HTML.png)
Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities
![Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates](https://d3i71xaburhd42.cloudfront.net/1f43d4a4d71dceacf4883377158b52fc06059857/6-Figure2-1.png)
PDF] Computational models for predicting substrates or inhibitors of P- glycoprotein.
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