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Journal of Chemical Theory and Computation
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April 7, 2017
tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables
Mohammad M Sultan, Vijay S Pande
The Journal of Physical Chemistry. B
|
September 23, 2017
Transfer Learning from Markov Models Leads to Efficient Sampling of Related Systems
Mohammad M Sultan, Vijay S Pande
The Journal of Chemical Physics
|
September 10, 2018
Automated design of collective variables using supervised machine learning
Mohammad M Sultan, Vijay S Pande
Journal of Chemical Information and Modeling
|
February 9, 2024
Compositional Deep Probabilistic Models of DNA-Encoded Libraries
Benson Chen, Mohammad M Sultan, Theofanis Karaletsos
Nature Chemistry
|
July 11, 2018
Towards simple kinetic models of functional dynamics for a kinase subfamily
Mohammad M Sultan, Gert Kiss, Vijay S Pande
Journal of Chemical Information and Modeling
|
April 20, 2023
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries
Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, et al.
Journal of Chemical Theory and Computation
|
March 13, 2018
Transferable Neural Networks for Enhanced Sampling of Protein Dynamics
Mohammad M Sultan, Hannah K Wayment-Steele, Vijay S Pande
Journal of Chemical Theory and Computation
|
December 18, 2014
Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations
Mohammad M Sultan, Gert Kiss, Diwakar Shukla, et al.
The Journal of Chemical Physics
|
November 24, 2016
Optimized parameter selection reveals trends in Markov state models for protein folding
Brooke E Husic, Robert T McGibbon, Mohammad M Sultan, et al.
Scientific Reports
|
November 17, 2017
Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain
Mohammad M Sultan, Rajiah Aldrin Denny, Ray Unwalla, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 15) with videos related to
Sort By:
Page
of 2
Journal of Chemical Theory and Computation
|
April 7, 2017
tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables
Mohammad M Sultan, Vijay S Pande
The Journal of Physical Chemistry. B
|
September 23, 2017
Transfer Learning from Markov Models Leads to Efficient Sampling of Related Systems
Mohammad M Sultan, Vijay S Pande
The Journal of Chemical Physics
|
September 10, 2018
Automated design of collective variables using supervised machine learning
Mohammad M Sultan, Vijay S Pande
Journal of Chemical Information and Modeling
|
February 9, 2024
Compositional Deep Probabilistic Models of DNA-Encoded Libraries
Benson Chen, Mohammad M Sultan, Theofanis Karaletsos
Nature Chemistry
|
July 11, 2018
Towards simple kinetic models of functional dynamics for a kinase subfamily
Mohammad M Sultan, Gert Kiss, Vijay S Pande
Journal of Chemical Information and Modeling
|
April 20, 2023
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries
Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, et al.
Journal of Chemical Theory and Computation
|
March 13, 2018
Transferable Neural Networks for Enhanced Sampling of Protein Dynamics
Mohammad M Sultan, Hannah K Wayment-Steele, Vijay S Pande
Journal of Chemical Theory and Computation
|
December 18, 2014
Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations
Mohammad M Sultan, Gert Kiss, Diwakar Shukla, et al.
The Journal of Chemical Physics
|
November 24, 2016
Optimized parameter selection reveals trends in Markov state models for protein folding
Brooke E Husic, Robert T McGibbon, Mohammad M Sultan, et al.
Scientific Reports
|
November 17, 2017
Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain
Mohammad M Sultan, Rajiah Aldrin Denny, Ray Unwalla, et al.
Page
of 2