Toward the Evolutionary Optimisation of Small Molecules Within Coarse-Grained Simulations: Training Molecules to Hide Behind Lipid Head Groups.

Abstract

Exploring the vast chemical space of small molecules poses a significant challenge. We develop a new strategy to efficiently explore this space using coarse-grained toy-like molecules utilizing the Martini3 force field and graph representations. This yields initial proof-of-concept results for the approach enabling the identification of optimal molecules with specific properties targeting lipid bilayers. By leveraging genetic algorithms and coarse-grained molecular dynamics simulations, we demonstrate the potential of our method in designing simple, linear molecules. Our findings show a good convergence toward molecules with weak amphiphilic properties, resembling known (general anesthetic) molecules. While this study demonstrates the feasibility of our method, further refinement is needed to fully realize its potential and explore more complex molecular topologies. Nevertheless, these encouraging results suggest a new path for future research in small molecule discovery and design without relying on extensive data sets.