CGCompiler: Automated coarse-grained molecule parameterization via noise-resistant mixed-variable optimization

Coarse-grained force-fields (CG FF) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible non-bonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building block coarse-grained approach has gained a tremendous popularity over the last decade. The parameterization of molecules can be highly complex and often involves the selection and fine tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parameterization of a molecule within the building block CG approach is a mixed-variable optimization problem: The non-bonded interactions are discrete variables whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parameterizing molecules within the Martini 3 coarse-grained force-field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For sake of demonstration, we parameterize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded- and non-bonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduces a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve as well as automate parameterization of molecules within building block CG FFs, such as Martini.