Molecular dynamics simulation for all
By Scott A. Hollingsworth, Ron O. Dror. Neuron, 2018
2,691 citations recorded by the source corpus.
The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists—a trend particularly noticeable in , though certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe in practical terms the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.
Subfields: Atomic and Molecular Physics, and Optics, Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/45319ee22b9f5c66392e72bc8d85647ee8fe7278
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
In Molecular Biology
- Jeffrey J. Gray
- Alexandre M. J. J. Bonvin
- Sai Pooja Mahajan
- Rodrigo Vargas Honorato
- Tatu Pantsar
- Wenhao Gao
- Jeremias Sulam
- M. V. van Dijk
In Spectroscopy
Citations in the corpus
- Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, background): “Additional examples are also listed in the recent reviews by Cavalli and collaborators [44] and Hollingsworth and Dror [45].”
- Deep Learning in Protein Structural Modeling and Design (2020, background): “ The discriminator, on the other hand, is trained to foil such fooling. The original objective function as formulated in55 is: min G max D V(D;G) = E x˘p data(x)[logD(x)] + E z˘pz(z)[log(1 D(G(z)))]: (5) Training is performed by stochastic optimization of this dierentiable loss function. While intui”