# In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

## TL;DR

This study uses high-throughput coarse-grained molecular dynamics to establish linear relations between bulk partitioning coefficients and the potential of mean force, enabling rapid prediction of membrane insertion thermodynamics for over 400,000 compounds.

## Contribution

It introduces a scalable simulation approach linking experimental partitioning data to detailed thermodynamic profiles, facilitating large-scale drug-membrane interaction screening.

## Key findings

- Linear relationships between partitioning coefficients and PMF features.
- Prediction of membrane insertion structures from bulk measurements.
- Reduction of chemical space complexity through coarse-graining.

## Abstract

The partitioning of small molecules in cell membranes---a key parameter for pharmaceutical applications---typically relies on experimentally-available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force, but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400,000 compounds. The potential of mean force hereby becomes an easily accessible quantity---already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.