Rational discovery of cardiolipin-selective small molecules by coarse-grained high-throughput simulations

High throughput screening is a relevant tool for molecular discovery and design. We make use of efficient coarse-grained free-energy calculations in combination with active learning to streamline the identification of relevant physical and chemical properties that determine selectivity towards a given biomolecular target. Simplifying the molecular representation through a small set of bead types offers two advantages: first, many molecules map to the same CG representation and second, screening boils down to systematically varying among the set of CG bead types available. The gained information allows us to formulate design rules that can be used to identify candidate molecules for experimental validation.

We apply our approach to the identification of small molecules selectively binding to the phospholipid Cardiolipin (CL). CL plays a central role in membrane functions and dynamics, and CL abnormalities are implicated in diseases. The chemical-space exploration provides general design rules to further optimize selectivity over known CL probes. These design rules are corroborated by fluorescence anisotropy measurements of two conforming compounds. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.