2,603 research outputs found
Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework
© 2019 Author(s). As molecular scientists have made progress in their ability to engineer nanoscale molecular structure, we face new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects because it involves a complicated, highly correlated, and three-dimensional many-body dynamical choreography which is often nonintuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline various efforts to extend immersive technologies to the molecular sciences, and we introduce "Narupa," a flexible, open-source, multiperson iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn potential energy functions, biomolecular conformational sampling, protein-ligand binding, reaction discovery using "on-the-fly" quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances and outline how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies such as iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures
Interactivity:the missing link between virtual reality technology and drug discovery pipelines
The potential of virtual reality (VR) to contribute to drug design and
development has been recognised for many years. Hardware and software
developments now mean that this potential is beginning to be realised, and VR
methods are being actively used in this sphere. A recent advance is to use VR
not only to visualise and interact with molecular structures, but also to
interact with molecular dynamics simulations of 'on the fly' (interactive
molecular dynamics in VR, IMD-VR), which is useful not only for flexible
docking but also to examine binding processes and conformational changes.
iMD-VR has been shown to be useful for creating complexes of ligands bound to
target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2
main protease. In this review, we use the term 'interactive VR' to refer to
software where interactivity is an inherent part of the user VR experience
e.g., in making structural modifications or interacting with a physically
rigorous molecular dynamics (MD) simulation, as opposed to simply using VR
controllers to rotate and translate the molecule for enhanced visualisation.
Here, we describe these methods and their application to problems relevant to
drug discovery, highlighting the possibilities that they offer in this arena.
We suggest that the ease of viewing and manipulating molecular structures and
dynamics, and the ability to modify structures on the fly (e.g., adding or
deleting atoms) makes modern interactive VR a valuable tool to add to the
armoury of drug development methods.Comment: 19 pages, 3 figure
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not
only brain computations, but also brain plasticity as probabilistic inference.
But a model for that has been missing. We propose that inherently stochastic
features of synaptic plasticity and spine motility enable cortical networks of
neurons to carry out probabilistic inference by sampling from a posterior
distribution of network configurations. This model provides a viable
alternative to existing models that propose convergence of parameters to
maximum likelihood values. It explains how priors on weight distributions and
connection probabilities can be merged optimally with learned experience, how
cortical networks can generalize learned information so well to novel
experiences, and how they can compensate continuously for unforeseen
disturbances of the network. The resulting new theory of network plasticity
explains from a functional perspective a number of experimental data on
stochastic aspects of synaptic plasticity that previously appeared to be quite
puzzling.Comment: 33 pages, 5 figures, the supplement is available on the author's web
page http://www.igi.tugraz.at/kappe
Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations
International audienc
Space-time multiresolution approach to atomistic visualization
Time-varying three-dimensional positional atomistic data are rich in spatial and temporal information. The problem is to understand them. This work offers multiple approaches that enable such understanding. An interactive atomistic visualization system is developed integrating complex analyses with visualization to present the data on space-time multiresolution basis facilitating the information extraction and generate understanding. This work also shows the usefulness of such an integrated approach. The information obtained from the analyses represents the system at multiple length and time scales. Radial distribution function (RDF) provides a complete average spatial map of the distribution of the atoms in the system which is probed to explore the system at different length scales. Coordination environments and cluster structures are visualized to look at the short range structures. Rings are visualized to understand the medium range structure. Displacement data and covariance matrices are visualized to understand the dynamical behaviors. Combinations of rendering techniques including animation, color map, sphere, polygonal and ellipsoid representations, pathlines and glyphs are used during the visualization process. The three-dimensional atomic configurations are reproduced accurately during rendering because of their physical significance while attributes such as coordination number, coordination stability and atomic species lack direct physical relevance and provide additional flexibilities in rendering. The performance results show interactive frame rates are achievable for systems consisting upto a thousand atoms. Such systems are typical of the systems simulated using first principles molecular dynamics simulations. The effectiveness and the usefulness of this work are justified for complex material systems using silicate and oxide liquids for visual analyses. The exploratory approach taken here has not been reported anywhere else before. The major contributions of this works are: 1. A new approach to the atomistic visualization advocating a formal integration of data analyses into the visualization system to improve the effectiveness and also present an implementation of the exploratory atomistic visualization system with integrated spatio-temporal analytical techniques. 2. The modeling of coordination environments, stability of the coordination environments, clusters, ring structures and diffusion for individual atoms. 3. The use of the visualization system for visual analysis of various liquid mineral systems of geophysical relevance
Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
Machine learning (ML) of quantum mechanical properties shows promise for
accelerating chemical discovery. For transition metal chemistry where accurate
calculations are computationally costly and available training data sets are
small, the molecular representation becomes a critical ingredient in ML model
predictive accuracy. We introduce a series of revised autocorrelation functions
(RACs) that encode relationships between the heuristic atomic properties (e.g.,
size, connectivity, and electronegativity) on a molecular graph. We alter the
starting point, scope, and nature of the quantities evaluated in standard ACs
to make these RACs amenable to inorganic chemistry. On an organic molecule set,
we first demonstrate superior standard AC performance to other
presently-available topological descriptors for ML model training, with mean
unsigned errors (MUEs) for atomization energies on set-aside test molecules as
low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs
on set-aside test molecules in spin-state splitting in comparison to 15-20x
higher errors from feature sets that encode whole-molecule structural
information. Systematic feature selection methods including univariate
filtering, recursive feature elimination, and direct optimization (e.g., random
forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x
smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good
transferability to metal-ligand bond length prediction (0.004-5 {\AA} MUE) and
redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature
selection results across property sets reveals the relative importance of
local, electronic descriptors (e.g., electronegativity, atomic number) in
spin-splitting and distal, steric effects in redox potential and bond lengths.Comment: 43 double spaced pages, 11 figures, 4 table
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