Efficient calculation of molecular properties from simulation using kernel molecular dynamics.

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure-property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.