Computational compound screening of biomolecules and soft materials by molecular simulations

TL;DR

This review discusses how molecular dynamics simulations can be used for high-throughput screening of biomolecules and soft materials, emphasizing automation, machine learning, and database generation to understand structure-property relationships.

Contribution

It introduces a comprehensive MD-based screening framework incorporating automation and machine learning, specifically tailored for biomolecules and soft materials.

Findings

MD enables structure-property relationship analysis.

Machine learning accelerates compound screening.

Framework supports database creation and statistical insights.

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 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.