  Decades of hardware, methodological, and algorithmic development have
propelled molecular dynamics (MD) simulations to the forefront of
materials-modeling techniques, bridging the gap between electronic-structure
theory and continuum methods. The physics-based approach makes MD appropriate
to study emergent phenomena, but simultaneously incurs significant
computational investment. This topical review explores the use of MD outside
the scope of individual systems, but rather considering many compounds. Such an
in silico screening approach makes MD amenable to establishing coveted
structure--property relationships. We specifically focus on biomolecules and
soft materials, characterized by the significant role of entropic contributions
and heterogeneous systems and scales. An account of the state of the art for
the implementation of an MD-based screening paradigm is described, including
automated force-field parametrization, system preparation, and efficient
sampling across both conformation and composition. Emphasis is placed on
machine-learning methods to enable MD-based screening. The resulting framework
enables the generation of compound--property databases and the use of advanced
statistical modeling to gather insight. The review further summarizes a number
of relevant applications.
