Rational optimization of drug-membrane selectivity by computational screening

Mitochondria are organelles of eucaryiotic cells involved in a number of physiological pathways. Cardiolipin (CL) is a phospholipid unique to the inner mitochondrial membrane. It plays a central role in mitochondrial functions and dynamics, and CL abnormalities are implicated in diseases. Our goal is to find compounds with high selectivity that can act as CL probes.
We explore the capabilities of using a coarse-grained (CG) model to find structures with certain properties. The 5-bead-type reduced Martini force field (T5) is a physics-based model that incorporates both the essential chemical features with a robust treatment of statistical mechanics. It simplifies the molecular representation through a small set of bead types that encode a variety of functional groups. This 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. We have combined coarse-grained free energy calculations with deep representational learning and Bayesian optimization to efficiently screen the chemical space represented by all T5 compounds up to 400 Da molecular weight. The chemical-space exploration provides general design rules to further optimize selectivity over known CL probes.