Computational compound screening of biomolecules and soft materials by molecular simulations

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