SMartini: automated small molecule parametrization for Martini 3 force field

Abstract

We present SMartini, an automated pipeline for deriving Martini 3 coarse-grained force-field parameters for arbitrary small molecules. Starting from a molecular structure or a SMILES string, the pipeline maps the all-atom molecule onto a coarse-grained bead representation and automatically fits both bonded and non-bonded parameters. Bonded parameters are obtained via Boltzmann inversion of atomistic molecular dynamics trajectories; successive rounds of coarse-grained simulation and distribution-matching updates then refine these estimates until the coarse-grained conformational ensemble reproduces the all-atom reference within tolerance. We validate the pipeline on a diverse set of small molecules including drug-like compounds, metabolites, and cofactors. Implemented as a modular sequence of scripts that can be orchestrated on high-performance computing clusters, SMartini makes automated parametrization of large ligand libraries practical and reduces the manual effort required for coarse-grained model development, enabling high-throughput coarse-grained simulations of protein–ligand systems within the Martini 3 ecosystem. Significance The accurate simulation of protein–ligand interactions at physiologically relevant timescales remains a central challenge in computational biophysics and drug discovery. Coarse-grained models offer the necessary computational efficiency, but deriving reliable parameters for small molecules has traditionally been a labor-intensive, expert-driven process that limits throughput. We address this bottleneck by combining a partitioning algorithm with iterative all-atom-to-coarse-grained distribution matching, yielding a fully automated, reproducible pipeline that generates coarse-grained parameters in a fraction of the time required for manual parametrization.