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Omar N A Demerdash

Showing results (1-10 of 13) with videos related to

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Journal of Computer-Aided Molecular Design|October 28, 2021
Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose predictionOmar N A Demerdash
Proteins|March 22, 2012
Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysisOmar N A Demerdash, Julie C Mitchell
Proteins|June 14, 2013
Using physical potentials and learned models to distinguish native binding interfaces from de novo designed interfaces that do not bindOmar N A Demerdash, Julie C Mitchell
Proteins|August 18, 2010
ReplicOpter: a replicate optimizer for flexible dockingOmar N A Demerdash, Amanda Buyan, Julie C Mitchell
Plos Computational Biology|October 10, 2009
Structure-based predictive models for allosteric hot spotsOmar N A Demerdash, Michael D Daily, Julie C Mitchell
Frontiers in Immunology|September 2, 2024
TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasetsRajitha Rajeshwar T, Omar N A Demerdash, Jeremy C Smith
Proteins|September 17, 2013
Data-driven models for protein interaction and designXiaolei Zhu, Spencer S Ericksen, Omar N A Demerdash, et al.
Biophysical Journal|December 12, 2025
Allosteric prediction via convolutional neural networks and protein structural and dynamical featuresRajitha Rajeshwar T, John H Lagergren, Jeremy C Smith, et al.
Plos One|April 15, 2011
Expression of nestin by neural cells in the adult rat and human brainMichael L Hendrickson, Abigail J Rao, Omar N A Demerdash, et al.
The Journal of Physical Chemistry. B|March 10, 2026
Reparameterizing a Lipid Force Field Using Small-Angle X-ray Scattering to Improve Predictions of Multicomponent Membranes under Organic Solvent StressOmar N A Demerdash, Micholas Dean Smith, Lee-Ping Wang, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Journal of Computer-Aided Molecular Design|October 28, 2021
Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose predictionOmar N A Demerdash
Proteins|March 22, 2012
Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysisOmar N A Demerdash, Julie C Mitchell
Proteins|June 14, 2013
Using physical potentials and learned models to distinguish native binding interfaces from de novo designed interfaces that do not bindOmar N A Demerdash, Julie C Mitchell
Proteins|August 18, 2010
ReplicOpter: a replicate optimizer for flexible dockingOmar N A Demerdash, Amanda Buyan, Julie C Mitchell
Plos Computational Biology|October 10, 2009
Structure-based predictive models for allosteric hot spotsOmar N A Demerdash, Michael D Daily, Julie C Mitchell
Frontiers in Immunology|September 2, 2024
TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasetsRajitha Rajeshwar T, Omar N A Demerdash, Jeremy C Smith
Proteins|September 17, 2013
Data-driven models for protein interaction and designXiaolei Zhu, Spencer S Ericksen, Omar N A Demerdash, et al.
Biophysical Journal|December 12, 2025
Allosteric prediction via convolutional neural networks and protein structural and dynamical featuresRajitha Rajeshwar T, John H Lagergren, Jeremy C Smith, et al.
Plos One|April 15, 2011
Expression of nestin by neural cells in the adult rat and human brainMichael L Hendrickson, Abigail J Rao, Omar N A Demerdash, et al.
The Journal of Physical Chemistry. B|March 10, 2026
Reparameterizing a Lipid Force Field Using Small-Angle X-ray Scattering to Improve Predictions of Multicomponent Membranes under Organic Solvent StressOmar N A Demerdash, Micholas Dean Smith, Lee-Ping Wang, et al.
Pageof 2