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 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 machinelearning 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.

The Martini Model in Materials Science

“…This introduces a degeneracy in the CG representation, which translates into a reduction of the size of the chemical (compound) space. [284] Accordingly, this reduction of the size of the chemical space represents also a further speed-up which can help for screening studies. [284] Hence, we expect hybrid Martini/machine-learning schemes to be highly promising in order to efficiently explore the chemical space for different applications.…”