Computational high-throughput screening of drug-membrane thermodynamics

The thermodynamic partitioning of small molecules in lipid membranes is of fundamental importance for pharmaceutical applications. In silico, structural resolution over the molecule permeation can be obtained through the potential of mean force, which can be further employed to gain insights into the permeation kinetics. However, the extensive computational resources required by atomistic molecular dynamics simulations and the size of compound space hamper the possibility of utilizing the potential of mean force in computational drug screening. Coarse-grained models efficiently address both issues, as they significantly mitigate the computational expense while capturing the relevant physical properties, and reduce the size of chemical space. In this work, we introduce a high-throughput screening of compound space by means of coarse-grained molecular dynamics simulations. This allows us to identify simple relationships between bulk properties and key features of the potential of mean force. The potential of mean force thereby becomes an easily accessible quantity in drug-screening applications. By connecting the coarse-grained and atomistic compound spaces, we show that our results are representative of the transmembrane behavior of a set of more than 400000 small molecules.