Computational compound screening of biomolecules and soft materials by molecular simulations

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.

Automated summary

Decades of development in hardware, methodology, and algorithms have advanced molecular dynamics (MD) simulations to the forefront of materials modeling. While suitable for studying emergent phenomena, MD requires significant computational investment. This review explores the use of MD beyond individual systems, focusing on many compounds, particularly biomolecules and soft materials.