His primary areas of investigation include Molecular dynamics, Protein folding, Statistical physics, Markov chain and Computational chemistry. His work in the fields of Force field overlaps with other areas such as Villin. The various areas that Vijay S. Pande examines in his Protein folding study include Chemical physics, Folding, Crystallography, Folding and Protein structure.
His Statistical physics study combines topics in areas such as Markov process, Cluster analysis, Molecular biophysics, Energy and Kinetic energy. He interconnects Range, Independent component analysis, Data mining and Data science in the investigation of issues within Markov chain. Vijay S. Pande has included themes like Sequence and Thermodynamics in his Computational chemistry study.
Vijay S. Pande focuses on Molecular dynamics, Statistical physics, Protein folding, Markov chain and Chemical physics. His Molecular dynamics study improves the overall literature in Computational chemistry. His study ties his expertise on Molecular biophysics together with the subject of Statistical physics.
His Protein folding research is multidisciplinary, relying on both Crystallography, Folding, Protein structure and Folding. His work deals with themes such as Native state and Kinetics, which intersect with Folding. His work on Markov model as part of general Markov chain research is frequently linked to State model, thereby connecting diverse disciplines of science.
His primary areas of study are Artificial intelligence, Molecular dynamics, Machine learning, Artificial neural network and Markov chain. He undertakes interdisciplinary study in the fields of Molecular dynamics and Population through his works. Vijay S. Pande combines subjects such as Graph, Cheminformatics, Benchmark, Sequence and Drug discovery with his study of Machine learning.
His work carried out in the field of Markov chain brings together such families of science as Independent component analysis, Sampling, Statistical physics, Algorithm and Variational principle. Vijay S. Pande merges many fields, such as Statistical physics and Change size, in his writings. Hydrophobic collapse, Beta hairpin and Protein folding is closely connected to Native state in his research, which is encompassed under the umbrella topic of Chemical physics.
Vijay S. Pande spends much of his time researching Artificial intelligence, Machine learning, Software, Molecular dynamics and Algorithm. His Machine learning research is multidisciplinary, incorporating elements of Molecular machine, Data mining, Drug discovery and Benchmark. The Software study combines topics in areas such as Force field, Data science and Maxima and minima.
His research in Molecular dynamics intersects with topics in Extensibility, Statistical physics, Coding and Source code. His Statistical physics study incorporates themes from Operator, Reaction coordinate and Eigenfunction. His Algorithm research integrates issues from Encoding, Encoder, Brownian dynamics, Markov chain and Nonlinear system.
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CHARMM-GUI Input Generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM Simulations using the CHARMM36 Additive Force Field
Jumin Lee;Xi Cheng;Jason M. Swails;Min Sun Yeom.
Journal of Chemical Theory and Computation (2016)
Current Status of the AMOEBA Polarizable Force Field
Jay W. Ponder;Chuanjie Wu;Pengyu Ren;Vijay S. Pande.
Journal of Physical Chemistry B (2010)
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
Robert T. McGibbon;Kyle A. Beauchamp;Matthew P. Harrigan;Christoph Klein.
Biophysical Journal (2015)
Absolute comparison of simulated and experimental protein-folding dynamics
Christopher D. Snow;Houbi Nguyen;Vijay S. Pande;Martin Gruebele.
Molecular graph convolutions: moving beyond fingerprints
Steven M. Kearnes;Kevin McCloskey;Marc Berndl;Vijay S. Pande.
Journal of Computer-aided Molecular Design (2016)
Exploring the Helix-Coil Transition via All-Atom Equilibrium Ensemble Simulations
Eric J. Sorin;Vijay S. Pande.
Biophysical Journal (2005)
Screen Savers of the World Unite
Michael Shirts;Vijay S. Pande.
Random-coil behavior and the dimensions of chemically unfolded proteins
Jonathan E. Kohn;Ian S. Millett;Jaby Jacob;Jaby Jacob;Bojan Zagrovic.
Proceedings of the National Academy of Sciences of the United States of America (2004)
Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins
Michael R. Shirts;Jed W. Pitera;William C. Swope;Vijay S. Pande.
Journal of Chemical Physics (2003)
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu;Bharath Ramsundar;Evan N. Feinberg;Joseph Gomes.
Chemical Science (2018)
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