What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Statistics
- Enzyme
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.
His most cited work include:
- CHARMM-GUI Input Generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM Simulations using the CHARMM36 Additive Force Field (880 citations)
- Current Status of the AMOEBA Polarizable Force Field (723 citations)
- MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories (635 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Molecular dynamics (31.92%)
- Statistical physics (25.25%)
- Protein folding (23.03%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (9.70%)
- Molecular dynamics (31.92%)
- Machine learning (6.46%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- MoleculeNet: a benchmark for molecular machine learning (509 citations)
- OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. (387 citations)
- Low Data Drug Discovery with One-Shot Learning (262 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Enzyme
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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