English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Decades of hardware, methodological, and algorithmic development havepropelled molecular dynamics (MD) simulations to the forefront ofmaterials-modeling techniques, bridging the gap between electronic-structuretheory and continuum methods. The physics-based approach makes MD appropriateto study emergent phenomena, but simultaneously incurs significantcomputational investment. This topical review explores the use of MD outsidethe scope of individual systems, but rather considering many compounds. Such anin silico screening approach makes MD amenable to establishing covetedstructure--property relationships. We specifically focus on biomolecules andsoft materials, characterized by the significant role of entropic contributionsand heterogeneous systems and scales. An account of the state of the art forthe implementation of an MD-based screening paradigm is described, includingautomated force-field parametrization, system preparation, and efficientsampling across both conformation and composition. Emphasis is placed onmachine-learning methods to enable MD-based screening. The resulting frameworkenables the generation of compound--property databases and the use of advancedstatistical modeling to gather insight. The review further summarizes a numberof relevant applications.

Computational compound screening of biomolecules and soft materials by molecular simulations